INTRODUCTIONThere is no single method of measuring insulin resistance that is both accurate and can be easily performed by general researchers. We validate the accuracy of oral glucose insulin sensitivity (OGIS) in the Chinese by comparing the OGIS120 and OGIS180, homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (OUICKI) with steady-state plasma glucose (SSPG) in different glucose tolerance subjects.

MATERIALS AND METHODSWe enrolled 515 subjects, aged between 20 and 75 years old, during routine health evaluations. All subjects were divided into normal, obese, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D) groups. Participants had a 3-hour oral glucose tolerance test (OGTT) and SSPG with an insulin suppression test. The relationships between SSPG and OGIS120, OGIS180, HOMA-IR, and QUICKI were evaluated.

RESULTSThe normal group had the highest OGIS120, OGIS180 and lowest SSPG as compared with the other 4 groups. OGIS180, HOMA-IR and QUICKI in all 5 groups were significantly related to SSPG (r = 0.397-0.621, all P <0.05). OGIS120 in all 5 groups was not significantly related to SSPG (r = 0.003-0.226). Additionally, the r value of OGIS180 against SSPG was not higher than the other 2 insulin sensitivity surrogates from OGTT.

CONCLUSIONSAlthough OGIS180 was more accurate in estimating insulin sensitivity than OGIS120 in the Chinese, it was not superior to the traditional surrogates such as HOMA-IR or QUICKI.

Adult ; Aged ; Case-Control Studies ; China ; Female ; Glucose Tolerance Test ; methods ; Humans ; Insulin Resistance ; Male ; Middle Aged ; Prediabetic State ; diagnosis ; Young Adult

INTRODUCTIONDecreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).

MATERIALS AND METHODSIn total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.

RESULTSThe area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6%) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1%, 80.1%, and 85.1%, respectively).

CONCLUSIONA prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.

Adult ; Blood Glucose ; Cross-Sectional Studies ; Female ; Glucose ; metabolism ; Glucose Tolerance Test ; Humans ; Insulin Resistance ; Male ; Metabolic Syndrome ; metabolism ; Middle Aged ; Models, Statistical

The objectives of this cohort analysis were to explore the relationship between insulin resistance (IR) and the criteria for metabolic syndrome (MetS) and to evaluate the ability to detect IR in subjects fulfilling those criteria. We enrolled 511 healthy subjects (218 men and 283 women) and measured their blood pressure (BP), body mass index, high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and fasting plasma glucose levels. Insulin suppression testing was done to measure insulin sensitivity as the steady-state plasma glucose (SSPG) value. Subjects with an SSPG value within the top 25% were considered to have IR. The commonest abnormality was a low HDL-C level, followed by high BP. The sensitivity to detect IR in subjects with MetS was about 47%, with a positive predictive value of about 64.8%, which has higher in men than in women. In general, the addition of components to the criteria for MetS increased the predictive value for IR. The most common combination of components in subjects with MetS and IR were obesity, high BP, and low HDL-C levels. All of the components were positive except for HDL-C, which was negatively correlated with SSPG. The correlation was strongest for obesity, followed by high TG values. In subjects with MetS, sensitivity for IR was low. However, body mass index and TG values were associated with IR and may be important markers for IR in subjects with MetS.
Adult ; Aged ; *Biological Markers ; Blood Glucose/metabolism ; Blood Pressure ; Body Mass Index ; Cholesterol, HDL/blood ; Female ; Humans ; *Insulin Resistance ; Male ; Metabolic Syndrome X/*diagnosis/*epidemiology ; Middle Aged ; Obesity, Morbid/diagnosis/epidemiology ; Predictive Value of Tests ; Prevalence ; Risk Factors ; Sensitivity and Specificity ; Triglycerides/blood

INTRODUCTION: Advanced breast cancer (ABC) remains common in Singapore. In 2019, 22.1% of breast cancer patients presented with ABC in our institution. Despite increasing affluence and the advent of national mammographic screening, the incidence of ABC has not changed significantly. This suggests inherent differences in women who present late. We aim to explore the socio-economic background, knowledge and attitudes of women who present with ABC. METHODS: Between December 2013 and July 2015, 100 patients who presented consecutively with ABC in a tertiary institution in Singapore were recruited to participate in an interviewer-led questionnaire exploring psychosocial and economic issues. RESULTS: Among the 100 patients, 63 and 37 presented with stages 3 and 4 breast cancer respectively. Median age was 57 (27-86), 52% had at least secondary education, 53% had no formal employment and 71% were married; 88% were aware of breast cancer symptoms, 82% were aware that mammography can help detect cancer, 82% believed that current treatment modality for breast cancer is effective, 96% had never undergone a mammography and 52.9% felt mammograms were unnecessary. A total of 64% presented symptomatic from the breast tumour, with a median duration of 3 months. Many of the patients were aware of breast cancer symptoms and the utility of mammography. However, a group of patients did not comply with screening. This may be due to poor understanding about breast screening and detection in its asymptomatic phase. CONCLUSION Further public education to improve understanding of breast cancer and screening mammography may help to improve rates for earlier detection of breast cancer.

The impact the metabolic syndrome (MetS) components on the severity of insulin resistance (IR) has not been reported. We enrolled 564 subjects with MetS and they were divided into quartiles according to the level of each component; and an insulin suppression test was performed to measure IR. In males, steady state plasma glucose (SSPG) levels in the highest quartiles, corresponding to body mass index (BMI) and fasting plasma glucose (FPG), were higher than the other three quartiles and the highest quartiles, corresponding to the diastolic blood pressure and triglycerides, were higher than in the lowest two quartiles. In females, SSPG levels in the highest quartiles, corresponding to the BMI and triglycerides, were higher than in all other quartiles. No significant differences existed between genders, other than the mean SSPG levels in males were greater in the highest quartile corresponding to BMI than that in the highest quartile corresponding to HDL-cholesterol levels. The factor analysis identified two underlying factors (IR and blood pressure factors) among the MetS variables. The clustering of the SSPG, BMI, triglyceride and HDLcholesterol was noted. Our data suggest that adiposity, higher FPG and triglyceride levels have stronger correlation with IR and subjects with the highest BMI have the highest IR.
Waist-Hip Ratio ; Triglycerides/blood ; Middle Aged ; Metabolic Syndrome X/*metabolism ; Male ; *Insulin Resistance ; Humans ; Female ; Fasting/blood ; Cholesterol, HDL/blood ; Body Mass Index ; Blood Glucose/analysis ; Aged ; Adult