Glycemic effect of post-meal walking compared to one prandial insulin injection in type 2 diabetic patients treated with basal insulin: A randomized controlled cross-over study.

Studies demonstrate that post-meal walking decreases postprandial hyperglycemia in type 2 diabetic patients but it has never been tested with the active treatment comparator. The objective of this study was to determine the effect of post-meal walking on glycemic control compared with one prandial insulin in type 2 diabetic patients who failed basal insulin. A randomized controlled cross-over study of post-meal walking or one prandial insulin was done in type 2 diabetic patients who were being treated with basal insulin between May 2017 and March 2018. In post-meal walking group, patients walked after meal for 15-20 minutes one meal a day every day for 6 weeks. In prandial insulin (basal plus) group, one prandial insulin was injected before breakfast or main meal with rapid-acting insulin. The primary outcome was a difference in HbA1c reduction in post-meal walking compared with basal plus groups. Fourteen patients completed the study. By intention-to-treat analysis, HbA1c was reduced by -0.05(range:-1.08 to 0.74) and -0.19(range:-0.8 to 0.56) % in post-meal walking and basal plus groups respectively. By per-protocol analysis, post-meal walking and basal plus groups decreased HbA1c by 0.13(range:-0.74 to 1.08) and 0.2(range:-0.56 to 0.8) %, respectively. There was were no significant differences in HbA1c reduction from baseline in each group and between groups in both intention-to-treat and per-protocol analysis. Fructosamine levels were decreased by 17.5(-59 to 43) and 10(-15 to 40) µmol/L, respectively at 3 and 6 weeks in post-meal walking group whereas the respective changes in basal plus group were 12.5(-17 to 64) and 17.5(-28 to 38) µmol/L and there were no significant differences in fructosamine reduction from baseline in each group and between groups. In conclusion, although post-meal walking might be as effective as one prandial insulin to improve glycemic control in type 2 diabetic patients who failed basal insulin but the magnitude of reduction was small. A longer-term study with a larger sample size or with a different walking protocol is required.

Dysregulated lipid storage and its relationship with insulin resistance and cardiovascular risk factors in non-obese Asian patients with type 2 diabetes.

The prevalence of non-obese type 2 diabetes in Asians is up to 50%. This review aims to summarize the role of regional fat in the development of insulin resistance and cardiovascular risk in non-obese Asian type 2 diabetes as well as the role of intra-pancreatic fat and ß-cell dysfunction. The body fat content of non-obese Asian type 2 diabetic patients is not different from that of non-diabetic subjects but the proportion of intra-abdominal and intra-hepatic fat are greater. Visceral fat contributes to insulin resistance and cardiovascular risk in non-obese Asian type 2 diabetes. Intra-hepatic fat and the hypertrophic abdominal subcutaneous adipocytes are associated with insulin resistance and cardiovascular risk in non-obese, non-diabetic Asian subjects. It may be true in non-obese Asian type 2 diabetic patients. The role of intra-myocellular lipid and insulin resistance is uncertain. Intra-pancreatic fat may not be involved in ß-cell dysfunction in non-obese Asian type 2 diabetes.

Lean mass inversely predicts plasma glucose levels after oral glucose load independent of insulin secretion or insulin sensitivity in glucose intolerance subjects.

Muscle mass inversely relates to 2 hours glucose levels after oral glucose load in non-diabetic subjects. A study in glucose intolerance subjects has never been performed. We conducted this study to determine the relationship between muscle mass and glucose level after oral glucose load in glucose intolerance subjects. Sixty Thai subjects, 44 drug-naïve, newly diagnosed type 2 diabetes mellitus and 16 impaired glucose tolerance were studied. The 180 min 75 g oral glucose tolerance test was performed. Total body fat and lean mass were measured by dual-energy x-ray absorptiometry. Insulin sensitivity was determined by insulin sensitivity index using model of Matsuda & DeFronzo. The 1st-phase and total insulin secretion were determined from glucose tolerance data. Pearson correlation and linear regression were used for the analysis. Lean mass was inversely correlated with area-under-curves of glucose 0-180 min (r =-0.320; p=0.013). The relationship was significant after adjustment with age and body-mass-index (r =-0.350; p=0.007). Area-under-curves of glucose 0-180 min was correlated with height (r =-0.282; p=0.029), fasting glucose (r =0.742; p<0.0001), log area-under-curves of insulin 0-180 min (r =-0.258; p=0.047) and log 1st-phase insulin secretion (r =-0.518; p<0.0001). By multivariate analysis, fasting glucose (standardized ß=4.54; p<0.001), log 1st-phase insulin secretion (standardized ß=-43.09; p=0.005) and lean mass (standardized ß=-0.003; p=0.011) were the significant parameters predicting area-under-curves of glucose 0-180 min. In conclusion, lean mass inversely predicted glucose levels after oral glucose load independent of insulin secretion and insulin sensitivity in glucose intolerance subjects.

Diagnostic performance of 2 h postprandial capillary and venous glucose as a screening test for abnormal glucose tolerance.

AIMS: To evaluate the diagnostic performance of postprandial venous and capillary glucose to screen for abnormal glucose tolerance in primary care setting. METHODS: Both post-breakfast venous plasma and capillary blood glucose were taken simultaneously from a consecutive sample of volunteer civil service workers in Khon Kaen, Thailand between June and December 2009. The 75-g oral glucose tolerance test was performed within 3 days of the baseline visit. Both postprandial capillary and venous glucose were assessed for sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and likelihood ratio using the oral glucose tolerance test (OGTT) results for the diagnosis of abnormal glucose tolerance as a gold standard. RESULTS: 1102 volunteers participated, of whom 874 (79.3%) completed the full study protocol. Five-hundred and four (57.8%) of 874 participants were female. The mean age was 39.9 years (SD=12.16) and the mean BMI was 24.3 kg/m(2) (SD=6.86). The sensitivity and specificity at the optimal cut-off point for venous glucose were 68.28% (95% CI 60.04-75.75) and 67.90% (95% CI 64.38-71.28), respectively. The sensitivity and specificity at the optimal cut-off point for capillary glucose were 63.45% (95% CI 55.05-71.28) and 64.06% (95% CI 60.46-67.55), respectively. The area under the ROC curve was 0.73 (95% CI 0.68-0.78) for venous glucose and 0.69 (95% CI 0.64-0.74) for capillary glucose. The subgroup analysis involving individuals with waist circumference>90 cm improved the area under the curve (AUC) to 0.76 (95% CI 0.68-0.83). CONCLUSIONS: Postprandial blood glucose testing had a moderate discriminating characteristic for the diagnosis of abnormal glucose tolerance. Careful consideration is needed when using it to screen for this condition in general population.

Contribution of skeletal muscle mass on sex differences in 2-hour plasma glucose levels after oral glucose load in Thai subjects with normal glucose tolerance.

Women have higher 2-hour plasma glucose levels after oral glucose challenge than men. The smaller skeletal muscle mass in women may contribute to the higher postload glucose levels. The objective of this study was to test the hypothesis that the different amount of skeletal muscle mass between men and women contributed to sex difference in postload plasma glucose levels in subjects with normal glucose tolerance. Forty-seven Thai subjects with normal glucose tolerance, 23 women and 24 age- and body mass index-matched men, were studied. Body fat, abdominal fat, and appendages lean mass were measured by dual-energy x-ray absorptiometry. Skeletal muscle insulin sensitivity was determined by euglycemic-hyperinsulinemic clamp. First-phase insulin secretion and hepatic insulin sensitivity were determined from oral glucose tolerance data. beta-Cell function was estimated from the homeostasis model assessment of %B by the homeostasis model assessment 2 model. Correlation and linear regression analysis were performed to identify factors contributing to variances of postload 2-hour plasma glucose levels. This study showed that women had significantly higher 2-hour plasma glucose levels and smaller skeletal muscle mass than men. Measures of insulin secretion and insulin sensitivity were not different between men and women. Male sex (r = -0.360, P = .013) and appendages lean mass (r = -0.411, P = .004) were negatively correlated with 2-hour plasma glucose, whereas log 2-hour insulin (r = 0.571, P < .0001), total body fat (r = 0.348, P = .016), and log abdominal fat (r = 0.298, P = .042) were positively correlated with 2-hour plasma glucose. The correlation of 2-hour plasma glucose and sex disappeared after adjustment for appendages lean mass. By multivariate linear regression analysis, log 2-hour insulin (beta = 18.9, P < .0001), log 30-minute insulin (beta = -36.3, P = .001), appendages lean mass (beta = -1.0 x 10(-3), P = .018), and hepatic insulin sensitivity index (beta = -17.3, P = .041) explained 54.2% of the variance of 2-hour plasma glucose. In conclusion, the higher postload 2-hour plasma glucose levels in women was not sex specific but was in part a result of the smaller skeletal muscle mass. The early insulin secretion, hepatic insulin sensitivity, and skeletal muscle mass were the significant factors negatively predicting 2-hour postload plasma glucose levels in Thai subjects with normal glucose tolerance.

Hepatic fat content is a determinant of postprandial triglyceride levels in type 2 diabetes mellitus patients with normal fasting triglyceride.

Postprandial hypertriglyceridemia is common in type 2 diabetes mellitus (T2D). Significant numbers of T2D patients who have normal fasting triglyceride (TG) have postprandial hypertriglyceridemia. The role of regional adipose tissue and adiponectin on postprandial TG responses in this group of T2D patients is unclear. This study aimed to examine the contribution of regional adipose tissue and adiponectin to the variation of postprandial TG responses in T2D patients who have normal fasting TG levels. Thirty-one Thai T2D patients who had fasting TG<1.7 mmol/L were studied. All were treated with diet control or sulphonylurea and/or metformin. None was treated with lipid-lowering agents. Mixed-meal test was performed after overnight fast. Plasma glucose, insulin, and TG were measured before and 1, 2, 3, and 4 hours after the test. Adiponectin was measured in fasting state. Visceral as well as superficial and deep subcutaneous abdominal adipose tissues were determined by magnetic resonance imaging, and hepatic fat content (HFC) was determined by magnetic resonance spectroscopy. Univariate and multivariate regression analyses of postprandial TG and regional adipose tissue and metabolic parameters were performed. The TG levels before and 1, 2, 3, and 4 hours after the mixed meal were 1.32+/-0.40 (SD), 1.40+/-0.41, 1.59+/-0.40, 1.77+/-0.57, and 1.80+/-0.66 mmol/L, respectively (P<.0001). The area under the curve (AUC) of postprandial TG was positively and significantly correlated with fasting TG (r=0.84, P<.0001) and log.HFC (r=0.456, P=.033) and was inclined to be correlated with log.deep subcutaneous adipose tissue (r=0.38, P=.05) and sex (r=0.326, P=.073). The AUC of postprandial TG was not correlated with age, body mass index, waist circumference, log.superficial subcutaneous adipose tissue, log.visceral adipose tissue, hemoglobin A1c, fasting glucose, AUC.glucose, log.fasting insulin, log.AUC.insulin, log.homeostasis model assessment%B, log.homeostasis model assessment of insulin resistance, and adiponectin. Only fasting TG (beta=.815, P<.0001) and log.HFC (beta=.249, P=.035) predicted AUC of postprandial TG in regression model (adjusted R2=0.84, P<.0001). In conclusion, in T2D patients with normal fasting TG, the increase of postprandial TG levels is directly determined by fasting TG level and the amount of hepatic fat.

Gender differences of regional abdominal fat distribution and their relationships with insulin sensitivity in healthy and glucose-intolerant Thais.

To determine gender differences of regional abdominal fat distribution and their relationships with insulin sensitivity in healthy and glucose-intolerant Thais, 44 subjects, 22 men and 22 body mass index-matched women, with normal and abnormal glucose tolerance, which included subjects with impaired glucose tolerance and diabetes, were studied. Total body fat and total abdominal fat (TAF) at L1-L4 were measured by dual-energy x-ray absorptiometry. Regional abdominal fat, which consists of sc abdominal fat and visceral abdominal fat, was determined by single-slice computerized tomography of the abdomen at L4-L5 disc space level. Insulin sensitivity was determined by euglycemic hyperinsulinemic clamp and expressed as glucose infusion rate (GIR). With comparable body mass index, visceral abdominal fat was most strongly correlated with GIR after adjustment with percent total body fat in both healthy (r = -0.8155; P = 0.007) and glucose-intolerant women (r = -0.7597; P = 0.011), whereas TAF was most strongly correlated with GIR in both healthy (r = -0.8114; P = 0.008) and glucose-intolerant men (r = -0.6194; P = 0.101). By linear regression analysis, visceral abdominal fat accounted for 35.0% (beta = -3.53 x 10(-2); P = 0.001) of GIR variance in women, whereas TAF accounted for 39.3% (beta = -1.28 x 10(-4); P < 0.0001) of GIR variance in men. We conclude that there are gender differences in the relationships of regional abdominal fat and insulin sensitivity in slightly obese healthy and glucose-intolerant Thais, the difference of which may possibly be in part due to the difference of abdominal fat patterning between genders.

Regional abdominal fat distribution in lean and obese Thai type 2 diabetic women: relationships with insulin sensitivity and cardiovascular risk factors.

To determine the relationships of body fat distribution and insulin sensitivity and cardiovascular risk factors in lean and obese Thai type 2 diabetic women, 9 lean and 11 obese subjects, with respective mean age 41.7 +/- 6.3 (SD) and 48.0 +/- 8.5 years, and mean body mass index (BMI) 23.5 +/- 1.8 and 30.3 +/- 3.7 kg/m2, were studied. The amount of total body fat (TBF) and total abdominal fat (AF) were measured by dual-energy x-ray absorptiometer, whereas subcutaneous (SAF) and visceral abdominal fat areas (VAF) were measured by computerized tomography (CT) of the abdomen at the L4-L5 level. Insulin sensitivity was determined by euglycemic hyperinsulinemic clamp. Cardiovascular risk factors, which included fasting and post-glucose challenged plasma glucose and insulin, systolic (SBP) and diastolic blood pressure (DBP), lipid profile, fibrinogen, and uric acid, were also determined. VAF was inversely correlated with insulin sensitivity as determined by glucose infusion rate (GIR) during the clamp, in both lean (r=-0.8821; P=.009) and obese subjects (r=-0.582; P=.078) independent of percent TBF. SAF and TBF were not correlated with GIR. With regards to cardiovascular risk factors, VAF was correlated with SBP (r=0.5279; P=.024) and DBP (r=0.6492; P=.004), fasting insulin (r=0.7256; P=.001) and uric acid (r=0.4963; P=.036) after adjustment for percent TBF. In contrast, TBF was correlated with fasting insulin (r=0.517; P=.023), area under the curve (AUC) of insulin (r=0.625; P=.004), triglyceride (TG) (r=0.668; P=.002), and uric acid (r=0.49; P=.033). GIR was not correlated with any of cardiovascular risk factors independent of VAF. In conclusion, VAF was a strong determinant of insulin sensitivity and several cardiovascular risk factors in both lean and obese Thai type 2 diabetic women.

Causes of death, incidence and risk factors of cardiovascular diseases in Thai type 2 diabetic patients: a 5 year follow-up study.

There appear to be ethnic disparities in frequencies of diabetic complications in type 2 diabetic patients and such data from Asian countries are relatively few and limited. Thai type 2 diabetic patients who attended the diabetic clinic at Prince of Songkla University hospital during January-December 1997 and had no history of coronary heart disease (CHD) and stroke were studied to determine cause of death and to establish the incidence of and risk factors for cardiovascular disease (CVD). All patients were followed to death or to the end of year 2001. End-points included death from any cause, fatal and nonfatal CHD, fatal and nonfatal stroke and lower-extremity amputation. There were 229 patients who were followed for 4.2+/0.7 (S.D.) years (range: 0.6-5.0) with total follow-up period 958.2 patient-years. Twenty-nine patients died during follow-up; the total mortality rate was 30.3 (95%CI 20.2-43.4)/1000 patient-years. Of these, 9(9.4/1000 patient-years; 95%CI 4.3-17.8) died from sepsis, 7(7.3/1000 patient-years; 95%CI 2.9-15.0) from CVD, 5(5.2/1000 patient-years; 95%CI 2.7-12.2) from end-stage renal disease, 3(3.1/1000 patient-years; 95%CI 0.6-9.2) from malignancy and 1(1.0/1000 patient-years; 95%CI 0.03-5.8) from peripheral vascular disease. The incidences of fatal and nonfatal CHD as well as fatal and nonfatal stroke were 21.4(95%CI 13.0-33.0)/1000 and 12.8(95%CI 6.6-22.4)/1000 patient-years, respectively whereas the incidence of lower-extremity amputation was 4.3(95%CI 1.2-10.9)/1000 patient-years. Age, the presence of proteinuria and serum HDL-C < or = 0.9 mmol/l were independent risk factors of CHD with the respective Hazard ratios 1.09(95%CI: 1.02-1.17; P=0.016), 4.41(95%CI: 1.18-16.45; P=0.027) and 3.91(95%CI: 1.20-12.80; P=0.024). In conclusion, sepsis and CVD were the major causes of death accounting for approximately 50% of total mortality in Thai type 2 diabetic patients. Age, the presence of proteinuria and low HDL-C were independent risk factors for the development of CHD. The mortality from and the incidence of CHD in Thai type 2 diabetic patients are lower than those reported from Caucasian populations but the incidence of stroke appears to be higher. These findings need to be confirmed by a large-scale population-based study.

Relationships of body fat distribution, insulin sensitivity and cardiovascular risk factors in lean, healthy non-diabetic Thai men and women.

In order to study the relationships of body fat distribution, insulin sensitivity and cardiovascular risk factors in lean, healthy non-diabetic Thai men and women, 32 healthy, non-diabetic subjects, 16 men and 16 women, with respective mean age 28.4+/-6.6 (S.D.) and 32.8+/-8.9 years, mean BMI 21.0+/-2.8 and 21.2+/-3.7 kg/m(2), were measured for total body fat and abdominal fat by dual energy X-ray absorptiometry (DEXA), anthropometry and insulin sensitivity by euglycemic hyperinsulinemic clamp. Cardiovascular risk factors included fasting and post-glucose challenge plasma glucose and insulin, blood pressure, lipid profile, fibrinogen and uric acid. For similar age and BMI, men had a lower amount and percent of total body fat, but had a higher proportion of abdominal/total body fat than women. In men, insulin sensitivity, as determined by glucose infusion rate during euglycemic hyperinsulinemic clamp, was inversely correlated with total body fat, abdominal fat, BMI and waist circumference, whereas only total body fat, but not abdominal fat, BW and hip circumference were inversely correlated with insulin sensitivity in women. No cardiovascular risk factors, except area under the curve (AUC), of plasma insulin in women correlated with insulin sensitivity when adjusted for total body fat. After age adjustment, total body fat was better correlated with fasting and AUC of plasma glucose and insulin in men and with systolic blood pressure as well as triglyceride levels in women. Only HDL-C in men was better correlated with abdominal fat. In conclusion, there were sex-differences in body fat distribution and its relationship with insulin sensitivity and cardiovascular risk factors in lean, healthy non-diabetic Thai subjects. Total body fat was a major determinant of insulin sensitivity in both men and women, abdominal fat may play a role in men only. Body fat, not insulin sensitivity, was associated with cardiovascular risk factors in these lean subjects.

Novel insulin sensitivity index derived from oral glucose tolerance test.

The euglycemic hyperglycemic clamp is generally regarded as a reference method for assessing insulin sensitivity. However, this method is laborious and expensive. The oral glucose tolerance test (OGTT), the most commonly used method for evaluating whole body glucose tolerance, has often been used to assess insulin sensitivity. In the previous studies the correlation between the insulin sensitivity index (ISI) obtained from the OGTT (ISI(OGTT)) and those obtained from the glucose clamp (ISI(Clamp)) may not be satisfactory. This is because the glucose clamp study is designed for measuring peripheral glucose utilization, whereas plasma glucose responses during the OGTT are the results of peripheral glucose utilization and hepatic glucose production. Based on this problem, we developed a new equation, ISI(OGTT), [1.9/6 x body weight (kg) x fasting plasma glucose (mmol/liter) + 520 - 1.9/18 x body weight x area under the glucose curve (mmol/h.liter) - urinary glucose (mmol)/1.8] / [area under the insulin curve (pmol/h.liter) x body weight], which would represent peripheral glucose utilization only. We tested our equation with ISI(Clamp) and also compared with others. Thirty-three healthy volunteers (16 males) with normal glucose tolerance underwent a 75-g, 3-h OGTT on the morning of d 1 and a glucose clamp on the morning of d 2. Their mean (+/-SD) age and body mass index were 30.8 +/- 8.3 yr and 22.0 +/- 3.9 kg/m(2), respectively. The mean (+/-SD) glucose disposal rate and ISI determined by glucose clamp were 27.46 +/- 16.55 micro mol/kg.min and 7.39 +/- 2.72 micro mol/kg.min/pmol.liter, respectively. Pearson's correlation coefficient between our ISI(OGTT) and ISI(Clamp) was 0.869 (P < 0.0001) which was stronger than those corresponding values calculated from HOMA, QUICKI, Belfiore, Cederholm, Gutt, Matsuda, and Stumvoll, the respective values of which were 0.404, 0.434, 0.643, 0.533, 0.584, 0.734, and 0.508. In conclusion, the ISI(OGTT) derived from our equation is more suitable than others in assessing insulin sensitivity in subjects with normal glucose tolerance. Further studies in subjects with impaired glucose tolerance and diabetes mellitus should be performed to confirm the validity of this equation.

Screening for microalbuminuria in type 2 diabetes: a reconsideration.

To study the effect of timing of urine collection in determination of microalbuminuria in type 2 diabetic patients, timed urine (night time and daytime) as well as spot urine (first morning and random morning) samples were collected from 44 type 2 diabetic patients, 21 with normoalbuminuria and 23 with microalbuminuria. The methods of spot urine albumin expression for microalbuminuria were also compared between albumin concentration (AC) and albumin to creatinine ratio (ACR). Night time albumin excretion rate (AER) was 16 per cent lower and daytime AER was 13 per cent higher than 24-h AER (p<0.001). Forty-one (93%) of both night time and daytime urine samples had results corresponding with 24-h AER. For the spot urine, expression as AC showed a slightly stronger correlation with 24-h AER than expression as ACR. The levels of albumin in random morning urine samples were 50 and 35 per cent significantly higher than those in first morning urine samples when expressed as AC and ACR, respectively. In conclusion, because of low diurnal variation of AER, either daytime or night time urine could be used for screening of microalbuminuria in type 2 diabetes. Since mean albumin levels obtained from random morning urine were higher than those obtained from first morning urine, the cut-off level should be set higher in random morning urine in order to give comparable sensitivity in predicting microalbuminuria. The spot urine, either first morning or random morning urine, had a good correlation with the 24-h AER whether expressed as AC or ACR. Given the cost of the latter, the authors suggested measuring spot urine AC instead of ACR for screening of microalbuminuria in type 2 diabetes.