Evaluation of fasting plasma insulin concentration as an estimate of insulin action in nondiabetic individuals: comparison with the homeostasis model assessment of insulin resistance (HOMA-IR)

Insulin-mediated glucose disposal varies severalfold in apparently healthy individuals, and approximately one-third of the most insulin resistant of these individuals is at increased risk to develop various adverse clinical syndromes. Since direct measurements of insulin sensitivity are not practical in a clinical setting, several surrogate estimates of insulin action have been proposed, including fasting plasma insulin (FPI) concentration and the homeostasis model assessment of insulin resistance (HOMA-IR) calculated by a formula employing fasting plasma glucose (FPG) and FPI concentrations. The objective of this study was to compare FPI as an estimate of insulin-mediated glucose disposal with values generated by HOMA-IR in 758 apparently healthy nondiabetic individuals. Measurements were made of FPG, FPI, triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) concentrations, and insulin-mediated glucose uptake was quantified by determining steady-state plasma glucose (SSPG) concentration during the insulin suppression test. FPI and HOMA-IR were highly correlated (r = 0.98, P < 0.001). The SSPG concentration also correlated to a similar degree (P < 0.001) with FPI (r = 0.60) and HOMA-IR (r = 0.64). Furthermore, the relationship between FPI and TG (r = 0.35) and HDL-C (r = ?0.40) was comparable to that between HOMA-IR and TG (r = 0.39) and HDL-C (r = ?0.41). In conclusion, FPI and HOMA-IR are highly correlated in nondiabetic individuals, with each estimate accounting for ~40 % of the variability (variance) in a direct measure of insulin-mediated glucose disposal. Calculation of HOMA-IR does not provide a better surrogate estimate of insulin action, or of its associated dyslipidemia, than measurement of FPI.     

Surrogate estimates of insulin sensitivity in subjects with hypertension

The purpose of the study is to compare surrogate estimates of insulin sensitivity with a directly measured insulin sensitivity index, steady-state plasma glucose (SSPG) from insulin suppression test (IST), in subjects with hypertension. Two hundred and twenty-eight hypertensive patients who received IST for SSPG were included for analysis. Estimates from fasting measurements alone, homeostasis model assessment for insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI)), and indices from fasting and/or 2 h samples (ISI(0,120) and ISI(TX)) were calculated. In addition to Pearson and partial correlations, variance-component models were used to test the relationship between surrogate estimates of insulin sensitivity and SSPG. A large proportion of variance owing to covariates in the variance-component models indicated the goodness of model fit, irrespective of the independence among variables. SSPG was positively correlated with logarithmic transformation (Log) (HOMA-IR) and negatively correlated with QUICKI, Log (ISI(0,120)) and ISI(TX) (all P<0.0001). Log (ISI(0,120)) seemed to have a better correlation with SSPG (r=-0.72) than other measures in partial correlation. The proportion of variance owing to all covariates of Log (ISI(0,120)) and ISI(TX) were larger than those of Log (HOMA-IR) and QUICKI in the variance-component models. After adjustments for demographic and obesity covariates, the proportion of variance explained by Log (ISI(0,120)) were largest among the surrogate measures in the variance-component models. Our results showed that ISI(0,120) and ISI(TX) correlated better with SSPG than those used fasting measures alone (HOMA-IR and QUICKI). Log (ISI(0,120)) currently showing the strongest association with SSPG than other estimates is adaptable for use in large studies of hypertension.     

Usefulness of plasma glucose and insulin concentrations in identifying patients with insulin resistance

In this study, a specific measurement of insulin-mediated glucose disposal was used in 490 healthy volunteers to classify subjects as being insulin resistant. We then made standard measurements of plasma glucose and insulin concentrations to see how useful they would be as surrogate markers of insulin resistance.     

Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model

OBJECTIVE: To compare insulin sensitivity (Si) from a frequently sampled intravenous glucose tolerance test (FSIGT) and subsequent minimal model analyses with surrogate measures of insulin sensitivity and resistance and to compare features of the metabolic syndrome between Caucasians and Indian Asians living in the UK. SUBJECTS: In all, 27 healthy male volunteers (14 UK Caucasians and 13 UK Indian Asians), with a mean age of 51.2+/-1.5 y, BMI of 25.8+/-0.6 kg/m(2) and Si of 2.85+/-0.37. MEASUREMENTS: Si was determined from an FSIGT with subsequent minimal model analysis. The concentrations of insulin, glucose and nonesterified fatty acids (NEFA) were analysed in fasting plasma and used to calculate surrogate measure of insulin sensitivity (quantitative insulin sensitivity check index (QUICKI), revised QUICKI) and resistance (homeostasis for insulin resistance (HOMA IR), fasting insulin resistance index (FIRI), Bennetts index, fasting insulin, insulin-to-glucose ratio). Plasma concentrations of triacylglycerol (TAG), total cholesterol, high density cholesterol, (HDL-C) and low density cholesterol, (LDL-C) were also measured in the fasted state. Anthropometric measurements were conducted to determine body-fat distribution. RESULTS: Correlation analysis identified the strongest relationship between Si and the revised QUICKI (r=0.67; P=0.000). Significant associations were also observed between Si and QUICKI (r=0.51; P=0.007), HOMA IR (r=-0.50; P=0.009), FIRI and fasting insulin. The Indian Asian group had lower HDL-C (P=0.001), a higher waist-hip ratio (P=0.01) and were significantly less insulin sensitive (Si) than the Caucasian group (P=0.02). CONCLUSION: The revised QUICKI demonstrated a statistically strong relationship with the minimal model. However, it was unable to differentiate between insulin-sensitive and -resistant groups in this study. Future larger studies in population groups with varying degrees of insulin sensitivity are recommended to investigate the general applicability of the revised QUICKI surrogate technique.     

Quantitative insulin sensitivity check index and the reciprocal index of homeostasis model assessment are useful indexes of insulin resistance in type 2 diabetic patients with wide range of fasting plasma glucose

The purpose of the study was to investigate whether quantitative insulin sensitivity check index (QUICKI) and the reciprocal index of homeostasis model assessment (1/HOMA-IR) are excellent surrogate indexes of insulin resistance in type 2 diabetic patients with various ranges of fasting plasma glucose. One hundred eight type 2 diabetic patients were divided into tertiles according to fasting levels of plasma glucose (FPG) [T1: 4.2 < or = FPG (mmol/liter) < 6.5, n = 36; T2: 6.5 < or = FPG < 8.1, n = 36; T3: 8.1 < or = FPG < or = 11.1, n = 36]. The association between QUICKI or 1/HOMA-IR and insulin resistance index assessed by euglycemic hyperinsulinemic clamp (Clamp-IR) was investigated in each group. QUICKI was strongly correlated with Clamp-IR in all groups (r = 0.615 in T1, r = 0.659 in T2, and r = 0.788 in T3; all subjects, r = 0.691; all P < 0.001). Reciprocal of HOMA-IR also highly correlated with Clamp-IR in all groups (r = 0.600, r = 0.721, and r = 0.730, respectively; all subjects, r = 0.685; all P < 0.001). In conclusion, QUICKI and the reciprocal index of HOMA were highly correlated with Clamp-IR in type 2 diabetic patients with relatively wide ranges of fasting plasma glucose.     

QUICKI does not accurately reflect changes in insulin sensitivity with exercise training.

A novel index of insulin sensitivity, the quick insulin sensitivity check index, termed QUICKI (1/[log (insulin) + log (glucose)]), was recently developed. We examined whether QUICKI accurately reflects changes in insulin sensitivity after exercise training, a perturbation known to improve insulin sensitivity. Sedentary, nondiabetic adults underwent a frequently sampled iv glucose tolerance test before and after 6 months of training. Insulin sensitivity was estimated from the glucose tolerance test using Bergman's minimal model (insulin sensitivity-minimal model), and QUICKI was calculated from basal insulin and glucose. Exercise increased (P = 0.003) insulin sensitivity-minimal model but did not change (P = 0.12) QUICKI. Before and after training, the rank-correlation between QUICKI and insulin sensitivity-minimal model was significant (r = 0.79, P = 0.0005; r = 0.56, P = 0.03, respectively). However, the rank-correlation between fasting insulin alone with insulin sensitivity-minimal model was as good (before training r = -0.77, P = 0.0009; after training r = -0.55, P = 0.03) as that between QUICKI and insulin sensitivity-minimal model. Fasting glucose was not related to insulin sensitivity-minimal model at either time. When difference scores (i.e. after pretraining values) were examined, neither QUICKI nor fasting insulin correlated with insulin sensitivity-minimal model (QUICKI vs. insulin sensitivity-minimal model r = 0.24, P = 0.39; fasting insulin vs. insulin sensitivity-minimal model r = -0.40, P = 0.14). We conclude that fasting insulin is equivalent to fasting insulin plus glucose (i.e. QUICKI) at estimating basal insulin sensitivity in nondiabetic adults. However, QUICKI does not accurately reflect exercise-induced changes in insulin sensitivity within individual subjects.     

Incorporation of the fasting plasma FFA concentration into QUICKI improves its association with insulin sensitivity in nonobese individuals

Insulin resistance plays a major role in the pathophysiology of diabetes and is associated with obesity and cardiovascular disease. Excellent methods exist for the assessment of insulin sensitivity in the laboratory setting, such as the glucose clamp. However, these methods are not suitable for large population studies, and, thus, surrogate estimates of insulin sensitivity based on measurements in a single blood sample have been developed. Recently an index based on the logarithm and the reciprocal of the insulin-glucose product (QUICKI) has been proposed. QUICKI correlated with insulin sensitivity across the entire spectrum of glucose tolerance, but its performance was less satisfactory in normal subjects. Aim of this study was to ascertain whether the inclusion of fasting plasma free fatty acids concentration into QUICKI improves its association with insulin sensitivity in nonobese subjects. To test this hypothesis, we performed a euglycemic hyperinsulinemic clamp [40 mU/(m(2).min)] in 57 young, healthy, nonobese individuals with (n = 17) or without (n = 40) first-degree relatives affected by type 2 diabetes (the former group being an in vivo model of mild insulin resistance). We then compared the clamp-based index of insulin sensitivity with both QUICKI and a revised QUICKI, the latter index including the contribution of fasting free fatty acid concentration as well. The revised QUICKI considerably improved the relationship with the clamp-based index of insulin sensitivity (r = 0.51, P < 0.0001) with respect to QUICKI (r = 0.27, P < 0.05). In addition, the revised QUICKI revealed a reduction of insulin sensitivity in the offspring of type 2 diabetes (10%; P < 0.006) that QUICKI was unable to detect (3%; P = 0.28). In conclusion, this study suggests that the incorporation of fasting free fatty acid level into QUICKI is useful to improve its correlation with the clamp-based index of insulin sensitivity and its discriminatory power in case of mild insulin resistance. Further investigation is needed to ascertain its applicability to patients with obesity and type 2 diabetes.     

Whole-body insulin sensitivity,low-density lipoprotein (LDL) particle size,and oxidized LDL in overweight,nondiabetic men

Insulin resistance is often accompanied by elevated plasma triglycerides (TG) and a preponderance of small, dense low-density lipoprotein (LDL) particles. However, it remains unclear whether or not insulin resistance is related to LDL particle size, independent of plasma TG. We sought to determine the strength of the relationships among these variables in a group of overweight, nondiabetic men (N = 34; body mass index [BMI], 25 to 35 kg/m(2); age, 50 to 75 years), as well as to examine the possible relation between insulin sensitivity and oxidized LDL (oxLDL). We also examined the strength of the relationships between these lipid variables and estimates of insulin sensitivity using calculated indices based on fasting insulin and glucose concentrations. Insulin sensitivity (Si) was significantly associated with total TG (r = -0.61, P <.001), very-low-density lipoprotein (VLDL)-TG (r = -0.60, P <.001), and LDL size (r =.414, P <.05). LDL size was also significantly associated with TG (r = -0.73, P <.001), VLDL-TG (r = -0.73, P <.001), high-density lipoprotein-cholesterol (HDL-C) (r = 0.65, P <.001), the quantitative insulin sensitivity check index (QUICKI) (rho = 0.46, P <.01), and the homeostatic model for the assessment of insulin resistance (HOMA-IR) (rho = -0.45, P <.01). Si was a significant predictor of LDL size, with age and BMI also independent contributors to the variance in LDL size (R(2) = 0.172). However, when TG and HDL-C were added to the model, Si was no longer a significant predictor of LDL size. The correlation between Si and oxLDL was weak, but stastically significant (rho = -0.40, P =.02). These data suggest that the relation between Si and LDL size is largely mediated by plasma TG, and that Si is only weakly related to oxLDL in overweight, nondiabetic men.     

Evaluation of various insulin sensitivity indices in lean idiopathic hirsutism patients

Hirsutism is characterized by excessive growth of terminal hair in a male pattern. Idiopathic hirsutism (IH) is a common cause of hirsutism. Since there are few data demonstrating IH is associated with insulin resistance, we tried to assess various insulin sensitivity indices in lean IH and compare with healthy subjects. A cross-sectional study was performed in 71 lean (BMI between 20-25 kg/m(2)) women (17-39 years old), 31 with IH and 40 healthy individuals. Blood glucose, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), hepatic insulin sensitivity (ISI (HOMA)), Quicky index, reciprocal fasting insulin resistance index, fasting Belfiore index, and fasting glucose/insulin ration (GIR) were estimated using a single fasting sample of glucose and insulin levels. Raynaud indices calculated using the mathematical estimation in a single fasting sample of insulin levels were determined and compared in two groups. Fasting insulin, Raynaud index, HOMA-IR and Fasting insulin resistance index (FIRI) results were higher in IH group than in controls (p<0.01, for all). Fasting Belfiore index, QUICKI index, ISI(HOMA) and FIRI(-1) results were lower in IH group than in controls (p<0.01, for all). Our study showed that lean IH patients were more insulin resistant than healthy subjects. We propose that insulin sensitivity indices are useful methods for measuring insulin resistance in IH.     

Detection of insulin resistance by simple quantitative insulin sensitivity check index QUICKI for epidemiological assessment and prevention.

The aim of the present study was to evaluate the recently defined simple insulin sensitivity check index QUICKI (Katz et al. 2000) for insulin resistance diagnostics in common clinical and epidemiological practice. Both the QUICKI (1/log insulin + log glycemia in mg/dL) and HOMA (insulin * glycemia in micromol/L/22.5) indexes were calculated from fasting values in 259 adult healthy volunteers and patients, and in 47 healthy and obese children of prepubertal age of both sexes. In adults, a fall in the QUICKI index (mean +/- SEM in healthy subjects = 0.366 +/- 0.029) as well as an increase in the HOMA index (in healthy subjects 1.57 +/- 0.87) corresponded to metabolic and clinical manifestations of insulin resistance in various groups of outpatients. The QUICKI index had lower dispersion variances and the 95% confidence limits displayed a higher discrimination capacity. Patients with glucose intolerance or diabetes, hyperlipidemia typical for insulin resistance, or with combination of these metabolic disorders were characterized by QUICKI index values that were significantly lower than those of healthy volunteers. The QUICKI index in healthy prepubertal children indicated a higher insulin resistance compared to adults (mean 0.339 +/- 0.020); an increase in the QUICKI index in obese children with BMI over 25 was not significant, although obese children showed a significant increase of serum leptin and triglycerides and a decrease of HDL-cholesterol. Adult patients with QUICKI index below 0.357 (which is at the lower limit of 95% confidence limits in healthy persons) represented a group with typical manifestations of metabolic syndrome, differing in these parameters significantly from the group of patients of comparable age with a QUICKI index greater than 0.357. The present study suggests suitability of the QUICKI index for diagnosis of insulin resistance in clinical and epidemiological practice. However, a normal QUICKI index range needs to be established for each laboratory with an appropriate control group because of significant interlaboratory variations in insulin determinations and/or possible differences in various populations.     

Methods for quantifying insulin resistance in human immunodeficiency virus-positive patients

Various indirect indices have been used in human immunodeficiency virus (HIV)-infected individuals to assess insulin resistance, but the validity of these measures has not been rigorously assessed by comparison with physiologic methods of quantifying insulin-mediated glucose uptake (IMGU). We directly measured IMGU in 50 nondiabetic HIV-positive subjects by determining the steady-state plasma glucose (SSPG) concentration in response to a 3-hour continuous infusion of insulin, glucose, and somatostatin. Because steady-state plasma insulin concentrations were similar (approximately 60 microU/mL) in all subjects, the SSPG concentrations provided direct assessments of insulin action. Relationships between SSPG levels and various surrogate measures of IMGU derived from the 75-g oral glucose tolerance test (OGTT) were determined. The indirect measure of IMGU most closely related to SSPG concentrations was the total integrated insulin response to a 75-g glucose load (r=0.78, P<.01), accounting for approximately two thirds of the variability in SSPG (r2=0.61). Other indirect measures of IMGU, including the homeostasis assessment for insulin resistance (HOMA-IR), were also significantly related to SSPG values, but had lower magnitudes of correlation (r=0.43 to 0.61), thereby possessing limited ability to predict SSPG variability (r2=0.18 to 0.37). In conclusion, indirect measures of IMGU need to be applied with caution when evaluating insulin action in HIV-infected patients.     

Fasting insulin concentration is highly correlated with quantitative insulin sensitivity check index

Given that the "gold standard" method for evaluating insulin sensitivity in vivo (hyperinsulinemic euglycemic glucose clamp technique) cannot be routinely applied because of technical reasons, simple methods and indexes were developed and are currently available to assess insulin sensitivity in vivo. Quantitative insulin sensitivity check index (QUICKI) has recently been described and is able to accurately estimate insulin sensitivity from a fasting blood sample. We demonstrated that fasting insulin levels strongly inversely correlated with QUICKI in three different groups: 215 healthy nondiabetic nonobese subjects, 62 nondiabetic obese subjects, and 44 patients with glucose intolerance or type 2 diabetes mellitus. Fasting insulin measurement is a simple way of assessing insulin sensitivity in obese and nonobese humans, with or without glucose intolerance or type 2 diabetes mellitus.     

Pravastatin does not affect insulin sensitivity and adipocytokines levels in healthy nondiabetic patients

BACKGROUND: The effect of statins on insulin resistance is controversial and poorly studied in nondiabetic subjects. In addition, the effect of statins on leptin and adiponectin has never been studied. METHODS: Forty healthy nondiabetic volunteers (22 men and 18 women) aged 28 to 72 were randomized either to placebo or pravastatin 40 mg daily for a 12-week period. Insulin resistance, assessed using the Quantitative Insulin Sensitivity Check Index (QUICKI), as well as serum leptin and adiponectin levels, was measured at baseline and at the end of therapy. RESULTS: Pravastatin treatment decreased total cholesterol, low-density lipoprotein cholesterol, and triglycerides levels by 24%, 32%, and 14%, respectively ( P < .05 for all), but did not affect glucose and insulin levels, the (QUICKI) index, and adiponectin and leptin levels. When stratification was performed according to QUICKI index or sex, no significant differences were observed in the prevalues and postvalues of leptin, adiponectin, or QUICKI index in the pravastatin group. Adiponectin, leptin, and QUICKI index were statistically higher in women than in men ( P < .001 for both variables). Adiponectin was negatively correlated with body mass index (BMI; r = -0.39, P < .05) and positively correlated with the QUICKI index ( r = 0.54, P < .001) and with high-density lipoprotein cholesterol ( r = 0.50, P < .01). The relation between adiponectin and QUICKI index remained significant after adjustment for sex and BMI ( P = .005 and P = .007, respectively). Leptin was only related to BMI ( r = 0.57, P < .001) and to sex ( P < .001) with no significant correlations with lipid parameters or QUICKI index. Both sex and BMI are independent predictors of leptin ( P < .001 and P < .001). CONCLUSION: A 12-week treatment with pravastatin 40 mg/d does not change the QUICKI index and leptin and adiponectin levels in healthy volunteers. In addition, our results emphasize the importance of sex and BMI in the determination of both adiponectin and leptin. Adiponectin was also related to QUICKI index, whereas this relation was not found with leptin.     

Surrogate estimates of insulin sensitivity in Chinese diabetic patients and their offspring.

AIMS: To evaluate the relationship between surrogate measures of insulin sensitivity and results from euglycaemic insulin clamp in Chinese diabetic patients and their offspring. METHODS: The study included 59 volunteers from 20 diabetic families. Each participant completed a 75-g oral glucose tolerance test (OGTT) and a euglycaemic insulin clamp. We tested the correlation of surrogate measures of insulin sensitivity with M-values and M/I ratios (the amount of glucose infused during 90-120 min of the clamp was defined as M, and the mean values of plasma insulin during 90-120 min as I) from the euglycaemic insulin clamp. These measures included fasting insulin (FPI), insulin at 120 min of OGTT, insulin area under the curve of OGTT, fasting glucose-to-insulin ratio, homeostasis model assessment for insulin sensitivity (HOMA-IR and HOMA %S) and the Matsuda-DeFronzo index from OGTT. RESULTS: The Matsuda-DeFronzo index closely correlated to M-value and M/I in 21 diabetic, 38 non-diabetic individuals and the 59 participants overall (with M-value, r = 0.68, 0.84 and 0.84; with M/I, r = 0.71, 0.72 and 0.75, respectively, all P < 0.001). Without OGTT, HOMA %S was a good surrogate index for diabetic (correlated to M-value and M/I, r = 0.71 and 0.68, P = 0.001) and for non-diabetic subjects (to M-value, r = 0.73; to M/I, r = 0.55, both P < 0.001). FPI was as good as HOMA %S and Matsuda-DeFronzo index. CONCLUSIONS: The Matsuda-DeFronzo index, HOMA %S and FPI are good surrogate estimates of insulin sensitivity in Chinese diabetic subjects and their offspring.     

Surrogate indexes vs. euglycaemic-hyperinsulinemic clamp as an indicator of insulin resistance and cardiovascular risk factors in overweight and obese postmenopausal women

BACKGROUND: There is considerable interest in validating the most convenient method to estimate insulin sensitivity in clinical research protocols that could best indicate cardiovascular risk factors. To address this issue we examined the interrelationships of several cardiovascular risk factors with surrogate indexes such as fasting insulin, the homeostasis model assessment (HOMA), the quantitative insulin sensitivity check index (QUICKI) and the revised QUICKI vs the euglycaemic-hyperinsulinemic (EH) clamp in a non-diabetic overweight or obese postmenopausal female population. DESIGN: Cross-sectional study involving 88 obese postmenopausal women (age: 57.5+/-5.0 yrs; body mass index: 32.52+/-4.4 kg/m2; percent body fat: 46.35+/-4.9%). METHODS: Insulin sensitivity was determined by the EH clamp technique as well as by surrogate indexes such as fasting insulin, HOMA, log HOMA, QUICKI and revised QUICKI. Body composition and body fat distribution were measured using dual energy x-ray absorptiometry and computed tomography, respectively. RESULTS: Correlations between insulin resistance indexes (fasting insulin, revised QUICKI, QUICKI, log HOMA, HOMA) vs glucose disposal were similar (range of r's=0.40 to 0.49), suggesting that no index was superior to another with respect to its relationship with the EH clamp. Correlations between the insulin resistance indexes with plasma lipids were comparable among all indexes, however, systolic blood pressure, visceral fat and C-reactive protein were moderately superior with index vs the EH clamp. CONCLUSION: Surrogate measures of insulin resistance, in particular fasting insulin, are simple tools appropriate for epidemiological studies that can be used as substitutes for the EH clamp to estimate glucose disposal and cardiovascular risk factors in overweight and obese postmenopausal women.     

Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women

The current study was initiated to determine whether insulin resistance and/or hyperinsulinemia affected the ability of obese individuals to lose weight in response to hypocaloric diets. Thirty-one obese, nondiabetic women, with values for body mass index ranging from 28.0-35.0 kg/m2, volunteered for this program. Resistance to insulin-mediated glucose disposal was assessed by determining their steady state plasma insulin and glucose concentration during the last 30 min of a 180-min infusion of somatostatin, insulin, and glucose. The total integrated insulin response to breakfast and lunch was also determined. After the baseline measurements, volunteers were placed on a hypocaloric diet calculated to lead to a minimum weekly loss of 1% of ideal body weight. Individuals who met the criteria after 30 days of dieting were defined as weight loss successes (n = 20) and continued on the diet for another 30 days. Individuals not meeting the criteria were designated as weight loss failures (n = 12) and were discharged from the study. There was a mean (+/-SEM) weight loss at 60 days of 9.2 +/- 0.4 kg in the 20 individuals defined as weight loss successes, but there was no correlation between weight loss and either steady state plasma glucose or the total integrated insulin response (r < 0.1; P > 0.83). Furthermore, using the same criteria to define insulin sensitivity and insulin resistance as those for therapeutic successes, the therapeutic failures comprised six insulin-sensitive and five insulin-resistant subjects. In summary, insulin-mediated glucose disposal varied widely in nondiabetic, obese women, and there was no relationship between baseline insulin resistance or total integrated insulin response and weight loss. It is concluded that the ability to lose weight on a calorie-restricted diet over a short time period does not vary in obese, healthy women as a function of insulin resistance or hyperinsulinemia.     

Evaluation of insulin resistance in acromegalic patients before and after treatment with somatostatin analogues

Many studies have recently shown that simple computer-solved indices, based on fasting glucose and insulin levels, closely mirror the euglycemic clamp technique in studying insulin resistance or pancreatic insulin secretion. Few data are at present available on the evaluation of these novel indices in acromegalic patients, known to be GH-dependent insulin-resistant subjects, in particular during medical treatment with somatostatin analogues. Indeed, these drugs are able to inhibit not only GH and IGF-I levels, but also insulin and glucagon pancreatic secretion, with contrasting effects on glucose metabolism. In this study, insulin resistance was evaluated by the homeostasis model assessment (HOMA-IR) and insulin sensitivity by quantitative insulin check index (QUICKI) in 27 normoglycemic acromegalic patients, before and after 6-month therapy with somatostatin analogues (lanreotide-SR 30-60 mg every 7-28 days in 15 and octreotide-LAR 20-30 mg every 28 days in 12). Thirty-five age- and sex-matched healthy subjects and 17 surgically treated acromegalic patients (5 cured and 12 not cured) were studied as control groups. Before medical treatment, HOMA-IR was higher in acromegalic patients than in healthy controls (4 +/- 3 vs 1.7 +/- 0.7, p < 0.05), while QUICKI was lower (0.33 +/- 0.04 vs 0.36 +/- 0.03, p < 0.05). During medical therapy, HOMA-IR decreased to 2.4 +/- 1.6 (p < 0.05) and became similar to that recorded in both healthy subjects and surgically treated patients. However, fasting glucose was increased and fasting insulin was decreased. QUICKI did not significantly change from basal values. No differences were observed between patients who normalized or not hormonal levels. The effects of the 2 drugs, though higher glucose levels were seen in patients treated with octreotide-LAR. In conclusion, this study demonstrates that medical treatment is able to improve insulin resistance, even if only successful surgery is able to completely normalize both HOMA-IR and QUICKI.     

Demonstration of a relationship between white blood cell count, insulin resistance, and several risk factors for coronary heart disease in women.

In order to evaluate the relationship between peripheral white blood cell (WBC) count, insulin-mediated glucose uptake, and several risk factors for coronary heart disease (CHD), WBC, plasma glucose and insulin responses to a 75-g oral glucose challenge, fasting plasma cholesterol, high-density-lipoprotein (HDL)-cholesterol, and triglyceride concentration, and systolic and diastolic blood pressure were determined in 63 consecutive female volunteers with normal glucose tolerance. The results demonstrated the presence of statistically significant correlation coefficients between WBC count and both insulin-mediated glucose disposal (r = 0.50, P less than 0.001) and insulin response to oral glucose (r = 0.50, P less than 0.001). Furthermore, WBC count correlated with plasma glucose response to oral glucose (r = 0.48, P less than 0.001), fasting plasma triglyceride (r = 0.37, P less than 0.005) and HDL-cholesterol concentrations (r = -0.38, P less than 0.005), and systolic (r = 0.22, P less than 0.1) and diastolic (r = 0.27, P less than 0.05) blood pressure. However, the only two variables significantly correlated with WBC count in multivariate regression analysis were insulin resistance (r = 0.49, P less than 0.01) and insulin response (r = 0.35, P less than 0.05). These data indicate that WBC count is significantly correlated with changes in carbohydrate and lipoprotein metabolism and blood pressure that increase the risk of CHD. However, it appears that these relationships are secondary to resistance to insulin-mediated glucose uptake and hyperinsulinaemia.     

Heritability and genetic correlations of insulin sensitivity measured by the euglycaemic clamp.

AIMS: Studies investigating genetic factors influencing insulin sensitivity/insulin resistance have measured this phenotype using a variety of methods. In this study, genetic correlations and heritability of insulin sensitivity measured using the euglycaemic hyperinsulinaemic clamp and related phenotypes were examined. METHODS: The study population included 818 non-diabetic individuals from 297 nuclear families. Genetic correlations and heritability estimates were calculated using variance components methods. RESULTS: Homeostasis model of insulin resistance (HOMA-IR) and fasting insulin were very highly phenotypically and genetically correlated (r = 0.99 and r = 0.99). HOMA-IR and insulin sensitivity measured with the euglycaemic clamp were only moderately genetically correlated (r = -0.53), suggesting that the two traits may be influenced, at least in part, by different genes. Heritabilities for fasting insulin (h2 = 0.36) and HOMA-IR (h2 = 0.38) were consistent with the published literature, but heritability for insulin sensitivity measured by the euglycaemic clamp was slightly lower than other published estimates (h(2) = 0.24). CONCLUSIONS: Because HOMA-IR (or fasting insulin) and insulin sensitivity measured with the euglycaemic clamp are not highly genetically correlated, they should not be used interchangeably in genetic studies. Given the very high correlations between fasting insulin and HOMA-IR, HOMA-IR does not offer any advantage over fasting insulin in analyses of insulin sensitivity in this population.     

Limitation of the validity of the homeostasis model assessment as an index of insulin resistance in Korea

Homeostasis model assessment of insulin resistance (HOMA-IR) is a less invasive, inexpensive, and less labor-intensive method to measure insulin resistance (IR) as compared with the glucose clamp test. The aim of this study was to evaluate the validity of HOMA-IR by comparing it with the euglycemic clamp test in determining IR. We assessed the validity of HOMA-IR by comparing it with the total glucose disposal rate measured by the 3-hour euglycemic-hyperinsulinemic clamp in subjects with type 2 diabetes (n = 47), impaired glucose tolerance (n = 21), and normal glucose tolerance (n = 22). There was a strong inverse correlation (r = -0.558; P < .001) between the log-transformed HOMA-IR and the total glucose disposal rate. There was moderate agreement between the 2 methods in the categorization according to the IR (weighted kappa = 0.294). The magnitude of the correlation coefficients was smaller in the subjects with a lower body mass index (BMI <25.0 kg/m2 , r = -0.441 vs BMI > or =25.0 kg/m2 , r = -0.615; P = .032), a lower HOMA-beta cell function (HOMA- beta <60.0, r = -0.527 vs HOMA- beta > or =60.0, r = -0.686; P = .016), and higher fasting glucose levels (fasting glucose < or =5.66 mmol/L, r = -0.556 vs fasting glucose >5.66 mmol/L, r = -0.520; P = .039). The limitation of the validity of the HOMA-IR should be carefully considered in subjects with a lower BMI, a lower beta cell function, and high fasting glucose levels such as lean type 2 diabetes mellitus with insulin secretory defects.