Direct Measures Of Insulin Sensitivity Research Articles

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

Insulin-resistance in paediatric age: Its magnitude and implications.

Insulin resistance (IR) is insulin failure in normal plasma levels to adequately stimulate glucose uptake by the peripheral tissues. IR is becoming more common in children and adolescents than before. There is a strong association between obesity in children and adolescents, IR, and the metabolic syndrome components. IR shows marked variation among different races, crucial to understanding the possible cardiovascular risk, specifically in high-risk races or ethnic groups. Genetic causes of IR include insulin receptor mutations, mutations that stimulate autoantibody production against insulin receptors, or mutations that induce the formation of abnormal glucose transporter 4 molecules or plasma cell membrane glycoprotein-1 molecules; all induce abnormal energy pathways and end with the development of IR. The parallel increase of IR syndrome with the dramatic increase in the rate of obesity among children in the last few decades indicates the importance of environmental factors in increasing the rate of IR. Most patients with IR do not develop diabetes mellitus (DM) type-II. However, IR is a crucial risk factor to develop DM type-II in children. Diagnostic standards for IR in children are not yet established due to various causes. Direct measures of insulin sensitivity include the hyperinsulinemia euglycemic glucose clamp and the insulin-suppression test. Minimal model analysis of frequently sampled intravenous glucose tolerance test and oral glucose tolerance test provide an indirect estimate of metabolic insulin sensitivity/resistance. The main aim of the treatment of IR in children is to prevent the progression of compensated IR to decompensated IR, enhance insulin sensitivity, and treat possible complications. There are three main lines for treatment: Lifestyle and behavior modification, pharmacotherapy, and surgery. This review will discuss the magnitude, implications, diagnosis, and treatment of IR in children.

Plasma Proteomics to Predict Insulin-mediated Glucose Disposal/Uptake

Background Direct measurement of insulin sensitivity (IS) by the hyperinsulinemic euglycemic clamp (HEC) technique is considered a ‘gold-standard’ method, but is laborious and expensive. High-throughput plasma proteomics might provide an opportunity to improve and streamline the prediction of IS. Methods We measured 828 proteins using the OLINK PEA-technique in baseline fasting plasma from 968 participants of the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study. We estimated the variance explained of the HEC-derived insulin- mediated glucose uptake (M-value, per kilogram of lean body mass) by clinical covariates alone, and applied LASSO regression to develop prediction models incorporating protein levels. Models were trained in 70% of the cohort and tested in the remaining 30%. Results Clinical covariates alone (age, sex, BMI, lipids, and blood pressure) explained 19.8% (95% confidence interval 15.3-24.2%) of the variance of the M-value. LASSO selected 50 proteins, which increased the variance explained in the testing set to 44.8% (95% CI 38.1-51.3%). A separate protein-only LASSO model of 46 proteins explained a similar proportion of variance (44.1%,95% CI 37.5-50.7%) as the clinical covariates and proteins combined. Conclusion & future directions Plasma proteomic profiling substantially improved the prediction of the M-value, which may allow for accurate assessment of IS without the need of HEC. We are currently evaluating the RISC based models in a replication cohort (the Uppsala Longitudinal Study of Adult Men, ULSAM).

Ethnic distinctions in the pathophysiology of type 2 diabetes: a focus on black African-Caribbean populations

Type 2 diabetes (T2D) is a global public health priority, particularly for populations of black African-Caribbean ethnicity, who suffer disproportionately high rates of the disease. While the mechanisms underlying the development of T2D are well documented, there is growing evidence describing distinctions among black African-Caribbean populations. In the present paper, we review the evidence describing the impact of black African-Caribbean ethnicity on T2D pathophysiology. Ethnic differences were first recognised through evidence that metabolic syndrome diagnostic criteria fail to detect T2D risk in black populations due to less central obesity and dyslipidaemia. Subsequently more detailed investigations have recognised other mechanistic differences, particularly lower visceral and hepatic fat accumulation and a distinctly hyperinsulinaemic response to glucose stimulation. While epidemiological studies have reported exaggerated insulin resistance in black populations, more detailed and direct measures of insulin sensitivity have provided evidence that insulin sensitivity is not markedly different to other ethnic groups and does not explain the hyperinsulinaemia that is exhibited. These findings lead us to hypothesise that ectopic fat does not play a pivotal role in driving insulin resistance in black populations. Furthermore, we hypothesise that hyperinsulinaemia is driven by lower rates of hepatic insulin clearance rather than heightened insulin resistance and is a primary defect rather than occurring in compensation for insulin resistance. These hypotheses are being investigated in our ongoing South London Diabetes and Ethnicity Phenotyping study, which will enable a more detailed understanding of ethnic distinctions in the pathophysiology of T2D between men of black African and white European ethnicity.

The Effect of a Diet Moderately High in Protein and Fiber on Insulin Sensitivity Measured Using the Dynamic Insulin Sensitivity and Secretion Test (DISST).

Evidence shows that weight loss improves insulin sensitivity but few studies have examined the effect of macronutrient composition independently of weight loss on direct measures of insulin sensitivity. We randomised 89 overweight or obese women to either a standard diet (StdD), that was intended to be low in fat and relatively high in carbohydrate (n = 42) or to a relatively high protein (up to 30% of energy), relatively high fibre (>30 g/day) diet (HPHFib) (n = 47) for 10 weeks. Advice regarding strict adherence to energy intake goals was not given. Insulin sensitivity and secretion was assessed by a novel method—the Dynamic Insulin Sensitivity and Secretion Test (DISST). Although there were significant improvements in body composition and most cardiometabolic risk factors on HPHFib, insulin sensitivity was reduced by 19.3% (95% CI: 31.8%, 4.5%; p = 0.013) in comparison with StdD. We conclude that the reduction in insulin sensitivity after a diet relatively high in both protein and fibre, despite cardiometabolic improvements, suggests insulin sensitivity may reflect metabolic adaptations to dietary composition for maintenance of glucose homeostasis, rather than impaired metabolism.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene.

Decreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol-stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity.

Identifying glucose thresholds for incident diabetes by physiological analysis: a mathematical solution.

Plasma glucose thresholds for diagnosis of type 2 diabetes are currently based on outcome data (risk of retinopathy), an inherently ill-conditioned approach. A radically different approach is to consider the mechanisms that control plasma glucose, rather than its relation to an outcome. We developed a constraint optimization algorithm to find the minimal glucose levels associated with the maximized combination of insulin sensitivity and β-cell function, the two main mechanisms of glucose homeostasis. We used a training cohort of 1,474 subjects (22% prediabetic, 7.7% diabetic) in whom insulin sensitivity was measured by the clamp technique and β-cell function was determined by mathematical modeling of an oral glucose tolerance test. Optimized fasting glucose levels were ≤ 87 and ≤ 89 mg/dl in ≤ 45-yr-old women and men, respectively, and ≤ 92 and ≤ 95 mg/dl in >45-yr-old women and men, respectively; the corresponding optimized 2-h glucose levels were ≤ 96, ≤ 98, ≤ 103, and ≤ 105 mg/dl. These thresholds were validated in three prospective cohorts of nondiabetic subjects (Relationship Between Insulin Sensitivity and Cardiovascular Disease Study, Botnia Study, and Mexico City Diabetes Study) with baseline and follow-up oral glucose tolerance tests. Of 5,593 participants, 452 progressed to diabetes. Similarly, in the three cohorts, subjects with glucose levels above the estimated thresholds had an odds ratio of 3.74 (95% confidence interval = 2.64-5.48) of progressing, substantially higher than the risk carried by baseline conventionally defined prediabetes [odds ratio = 2.32 (95% confidence interval = 1.91-2.81)]. The concept that optimization of glucose concentrations by direct measures of insulin sensitivity and β-cell function identifies gender- and age-specific thresholds that bear on disease progression is proven in a physiologically sound, quantifiable manner.

Response to Comment on: Harrison et al. β-Cell Function Preservation After 3.5 Years of Intensive Diabetes Therapy. Diabetes Care 2012;35:1406–1412

We appreciate the critical review of our article by Aizawa and Yamauchi (1), and we are pleased they found the study well designed and straightforward (2). Their critiques relate to 1 ) exclusion of reporting insulin sensitivity and 2 ) lack of a significant drug wash-out period prior to β-cell assessment. We did not perform a direct measure of insulin sensitivity using a hyperinsulinemic-euglyceimc clamp as this did not represent one of our main outcomes and would have added a significant technical and financial burden to the study. We attempted to estimate insulin sensitivity using indirect methods such as homeostasis model assessment of …

High-density lipoprotein levels are strongly associated with the recovery rate of insulin sensitivity during the acute phase of myocardial infarction: A study by euglycemic hyperinsulinemic clamp

The decrease of insulin sensitivity (IS) during myocardial infarction (MI) is strongly associated with increased morbidity and mortality. Recent data suggest that in individuals under stable conditions, high-density lipoprotein (HDL) may improve IS. To date, the role of HDL in the modulation of IS in acute metabolic stress conditions such as MI remains unknown. To explore the association between plasma HDL-C and the change in IS during the acute phase of MI. Consecutive nondiabetic patients with ST-segment elevation MI (n= 22) underwent direct measurement of IS through the euglycemic hyperinsulinemic clamp on the first morning and on the fifth day after onset of MI. Patients were grouped according to HDL-C levels at admission above and below the median value (35 mg/dL). At admission, there was no significant difference in baseline IS index, clinical, anthropometric, or treatment characteristics between low and high HDL groups. Between admission and fifth day, there was a decrease of 8% in IS index in the low HDL group and an 11% increase in the high HDL group (P= .001 for intragroup and P= .012 for intergroup difference). This difference remained significant after we controlled for the sex, age, waist circumference, triglycerides, baseline IS index, and statin dose during hospitalization. This is the first study to provide evidence that plasma levels of HDL-C are strongly associated with the recovery rate of IS during the acute phase of MI.

Abstract P149: A Large-scale Multi Ethnic Study of a Direct Measure of Insulin Sensitivity Demonstrates That South Asians are the Most Insulin Resistant Ethnic Group in the US

Introduction: Insulin resistance (IR) is a known risk factor for heart disease. Few studies have compared race/ethnic differences in IR using ‘gold standard’ direct measures of insulin sensitivity. Methods: A total of 892 non-diabetic subjects (548 White, 106 South Asian, 103 East Asian, 86 Hispanic and 49 Black) underwent a 4-hour insulin suppression test (IST) as a part of various IR related studies at Stanford over the last ∼20 years. We used generalized estimating equations assuming an exchangeable correlation structure to determine the association between race/ethnicity and steady state plasma glucose (SSPG) derived from an IST, accounting for correlation of outcomes among subjects from the same study. We similarly determined whether differences in plasma triglyceride (TG) and high-density lipoprotein (HDL) levels among race/ethnic groups could be explained by differences in SSPG. All analyses were adjusted for age, sex, and BMI. Results: Significant differences among the race/ethnic groups in SSPG were observed (p <0.001). South Asians were the most insulin resistant group with a mean increase in SSPG of 38 mg/dL, compared to whites after controlling for age, sex, and BMI, a difference equivalent to ∼1/2 of the standard deviation of SSPG. East Asians were the next most resistant group (mean +33 mg/dl SSPG compared to whites) followed by Hispanics (+20 mg/dl), Whites, and Blacks (−7 mg/dl). South Asians were the only group with significantly higher TG (mean +1.16 fold, p=0.04) and lower HDL (−3.0 mg/dl, p=0.02) levels compared to whites but these differences were no longer evident after controlling for SSPG. In contrast, Blacks had significantly lower TG (mean 0.8 fold, p = 0.006) compared to whites, but this difference was not at all mitigated after adjusting for SSPG. Blacks also had no significant differences in HDL compared to whites. Conclusions: Direct measures of insulin sensitivity suggest that South Asians are the most insulin resistant race/ethnic group in the US even after adjusting for the principal determinants of IR. IR may be largely responsible for differences in TG and HDL observed between South Asian and other race/ethnic groups. The etiologies behind differences in insulin sensitivity across race/ethnic groups remain to be determined.

Docosahexaenoic Acid Supplementation Improved Lipocentric but Not Glucocentric Markers of Insulin Sensitivity in Hypertriglyceridemic Men

Increase in obesity and metabolic syndrome are associated with increases in insulin resistance (IR) and type 2 diabetes mellitus. Results from animal intervention studies and human epidemiological studies suggest that n-3 polyunsaturated fatty acids can prevent and reverse IR, but results from human intervention studies have varied. Results from some human and animal studies suggest that docosahexaenoic acid (22:6n-3; DHA) may be more effective than eicosapentaenoic acid (20:5n-3; EPA) in the prevention of IR. By using a placebo-controlled, parallel study design, we examined the effects of DHA supplementation (3 grams/day, 90 days) in the absence of EPA on glucocentric and lipocentric markers of IR in hypertriglyceridemic men (n=14-17/group). DHA supplementation increased fasting plasma glucose concentration by 4.7% (P<0.05), but did not alter other indices of IR based on fasting (insulin and homeostasis model assessment of insulin resistance [HOMA-IR]) or postprandial insulin and glucose concentrations (areas under curves for insulin and glucose, Matsuda index). Glucose increased by 2.7% in the placebo group and was not significant; increases in glucose in the two groups did not differ from each other. DHA decreased circulating concentrations of several lipocentric markers of IR, including plasma concentrations of nonesterified fatty acids (13.0%), small, dense low-density lipoprotein (LDL) particles (21.7%), and ratio of tryglycerides to high-density lipoprotein cholesterol (TG/HDL-C) (34.0%) (P<0.05). None of the variables changed in the placebo group. Our results suggest that lipocentric markers of IR are more responsive to DHA supplementation than the glucocentric markers. Future studies with DHA in prediabetic subjects and direct measures of insulin sensitivity are needed.

The size of adrenal incidentalomas correlates with insulin resistance. Is there a cause-effect relationship?

Adrenal incidentalomas (AI) have often been associated with a high prevalence of insulin resistance (IR) and cardiovascular risk factors, although direct measurement of insulin sensitivity (IS) has never been carried out. We aimed to investigate whether the morphological and hormonal features of AI correlate with the presence and severity of IR, using the hyperinsulinaemic euglycaemic clamp (HEC). Forty patients with AI (22 women) with a mean age of 58.5±11.1 years underwent hormonal and morphological evaluation. Nineteen patients with AI without known history of diabetes mellitus (DM) or impaired glucose tolerance (IGT) and 17 matched controls underwent oral glucose tolerance test (OGTT) and hyperinsulinaemic euglycaemic clamp (HEC). Diabetes mellitus was observed in 13 patients (33%), while three (8%) had IGT. Thirty-one of the AI were nonfunctioning (82.5%), whereas two (5%) secreted cortisol (Cushing's syndrome) and seven (12.5%) showed subclinical secretion of cortisol. The 19 patients with nonfunctioning AI were more insulin resistant than controls (glucose up-take: 4.58±1.80 vs 5.85±2.48 mg/kg/min respectively; P=0.01); IS was inversely related to the mass size (r=-0.57; P=0.04), free urinary cortisol (r=-0.68; P=0.01), serum cortisol after 1-mg dexamethasone suppression (-0.65; P=0.02) and percentage of trunk fat mass (-0.77; P=0.02) and directly related to serum adreno cortico tropic hormone (ACTH) (r=0.62; P=0.03). After performing multivariate regression, the mass size was found to be the most powerful predictor of IR. Our study showed a high prevalence of insulin resistance in patients with nonfunctioning AI and suggests its possible involvement in AI growth.

Fasting urine pH is independent of insulin sensitivity

It has recently been suggested that a low urine pH be added to the abnormalities linked to insulin resistance. This conclusion is based on the finding of a low urine pH in individuals with clinical syndromes associated with insulin resistance and not on studies in which a direct measure of insulin sensitivity was shown to be significantly related to differences in urine pH. To address this issue, we quantified insulin-mediated glucose uptake (IMGU) by using the insulin suppression test in 96 apparently healthy, nondiabetic individuals and defined its relation to fasting urine pH. Urine samples were collected and analyzed from a cohort of healthy subjects within a narrow body mass index range who were recruited to determine insulin sensitivity. There was an approximate 6-fold variation in values for IMGU in this population, with no relation to urine pH (r = 0.02). Furthermore, there was no relation between body mass index, as a surrogate estimate of insulin resistance, and urine pH (r = 0.06). On the basis of these findings, we question the view that a low urine pH be added to the abnormalities linked to insulin resistance in low-risk populations.

Ethnicity and Weight Status Affect the Accuracy of Proxy Indices of Insulin Sensitivit

This study tested the hypotheses that correlations between direct measures of insulin sensitivity and proxy indices of insulin sensitivity derived from fasting values, (i) would not be affected by ethnicity, and (ii) would be stronger in overweight vs. weight-reduced states. We further hypothesized that associations between proxy indices and fat distribution would be similar to those between directly measured insulin sensitivity and fat distribution. Testing was performed in weight-stable conditions in 59 African-American (AA) and 62 white-American (WA) overweight, premenopausal women before and after a weight loss intervention. Subjects were retested 1 year following weight loss. Proxy indices were correlated against the insulin sensitivity index S(I) determined via minimal modeling. Fat distribution was assessed using computed tomography. Correlations between Si and proxy indices were consistently stronger among overweight women (r = 0.44-0.52) vs. weight-reduced women (r = 0.18-0.32), and among AA (r = 0.49-0.56, baseline; 0.24-0.36, weight-reduced) vs. WA (r = 0.38-0.46, baseline; 0.19-0.31, weight-reduced). Among subjects who regained >3 kg after 1 year, correlations between S(I) and proxy indices were similar to those observed at baseline, whereas correlations were weak among women who maintained their reduced body weight. S(I) and all proxy indices were similarly correlated with intra-abdominal adipose tissue (IAAT) at baseline, but not after weight loss. In conclusion, correlations between S(I) and proxy indices were affected by both ethnicity and weight status. If proxy indices are used in multiethnic populations, or in populations including both lean and overweight/obese subjects, data should be interpreted with caution.

Surrogate Markers of Insulin Resistance Are Associated with Consumption of Sugar-Sweetened Drinks and Fruit Juice in Middle and Older-Aged Adults ,

In this study, we examined the association between sugar-sweetened drink, diet soda, and fruit juice consumption and surrogate measures of insulin resistance. Sugar-sweetened drink, diet soda, and fruit juice consumption was estimated using a semiquantitative FFQ in 2500 subjects at the fifth examination (1991–1995) of the Framingham Offspring Study. Surrogate markers of insulin resistance measured in this study included fasting insulin, fasting glucose, homeostatic model assessment of insulin resistance, and the insulin sensitivity index (ISI0,120). Sugar-sweetened drink consumption was positively associated with fasting insulin (none vs. ≥2 servings/d, 188 vs. 206 pmol/L, P-trend <0.001) after adjusting for potential confounders. Sugar-sweetened drink consumption was not associated with fasting glucose or ISI0,120. Fruit juice consumption was inversely associated with fasting glucose (none vs. ≥2 servings/d, 5.28 vs. 5.18 mmol/L, P-trend = 0.006), but not with fasting insulin (none vs. ≥2 servings/d, 200 vs. 188 pmol/L, P-trend = 0.37) or ISI0,120 (none vs. ≥2 servings/d, 26.0 vs. 27.0, P-trend = 0.19) in multivariate models. Diet soda consumption was not associated with any surrogate measures of insulin resistance after adjustment for potential confounders (insulin: none vs. ≥2 servings/d, 195 vs. 193 pmol/L, P-trend = 0.59; glucose: 5.26 vs. 5.24 mmol/L, P-trend = 0.84; and ISI0,120: 26.2 vs. 26.7, P-trend = 0.37). In these healthy adults, sugar-sweetened drink consumption appears to be unfavorably associated with surrogate measures reflecting hepatic more than peripheral insulin sensitivity. Studies of long-term beverage consumption using more direct measures of insulin sensitivity are clearly warranted.

Point: HOMA—Satisfactory for the Time Being

There is no doubt that the cluster of clinical and metabolic features associated with insulin resistance predicts risk of developing type 2 diabetes and cardiovascular disease (CVD) (1); however, whether there is merit in defining a syndrome is less clear (2). There is also a lack of agreement regarding the value of measuring insulin sensitivity in clinical practice (3). The widely used criteria for defining the metabolic syndrome (those recommended by the World Health Organization [4] and the Adult Treatment Panel III [5]) includes a measure of insulin resistance in the definition of the syndrome. A very recent approach suggested by a group representing the International Diabetes Federation involves identifying individuals with central obesity and a range of clinical and metabolic parameters associated with insulin resistance; however, it does not include a direct measure of insulin sensitivity (6). It is argued that measures of insulin resistance do not predict CVD when other more easily measured components of the metabolic syndrome cluster are present, and these measures based on fasting insulin are unreliable (3). However, measurement of insulin sensitivity has greatly enhanced the understanding of the pathophysiology of diabetes and relationships to cardiovascular outcomes (7). Dismissing the assessment of insulin sensitivity because its measurement is too variable or difficult (3) is understandable, but hardly desirable, as one major dimension of altered metabolism will be omitted from research and clinical assessments. It may prove to be much the same as assessing cardiovascular risk without serum cholesterol. A truly reliable simple measure may prove to be more appropriate than an aggregation of surrogates in predicting subsequent development of diabetes and CVD and in further explaining disease progression and response to treatment. Several reviews (8,9) describe some of the numerous methods for measuring or estimating insulin sensitivity. The …

Validity of Glycemic Index Estimates in the Insulin Resistance Atherosclerosis Study

The article by Liese et al. (1) investigated the association of glycemic index, glycemic load, and total carbohydrate and fiber intake with direct measures of insulin sensitivity, insulin secretion, and adiposity in a cohort of 979 adults with normal and impaired glucose tolerance. The authors concluded that there were no significant associations of glycemic index, glycemic load, and carbohydrate intake with any measure of insulin sensitivity or secretion. In their study, usual dietary intake was assessed via a 114-item food frequency questionnaire (FFQ) that had been previously “validated” in a subsample of the Insulin Resistance Atherosclerosis Study (IRAS) population. Unfortunately, …

A comparison of the homeostasis model assessment (HOMA) with direct measurements of insulin resistance in pregnancy with and without gestational diabetes (GDM)

MEASUREMENTS OF INSULIN RESISTANCE IN PREGNANCY WITH AND WITHOUT GESTATIONAL DIABETES (GDM) MARTIN MONTORO, SIRI KJOS, ANNY XIANG, THOMAS A. BUCHANAN, University of Southern California, Women’s and Children’s Hospital, Dept. of OB/GYN, Los Angeles, California, HarborUCLA Med. Ctr., Dept. of OB/GYN (LA BioMed), Torrance, California, University of Southern California, Preventive Medicine, Los Angeles, California OBJECTIVE: To correlate third trimester HOMA [fasting serum glucose (mmol/L) ! fasting serum insulin (uU/mL)]/22.5 with insulin resistance and b-cell function in control and GDM women. STUDY DESIGN: 150 GDM and 25 non-diabetic women underwent (a) oral glucose tolerance test (OGTT), (b) intravenous glucose tolerance test (IVGTT) with minimal model analysis and (c) euglycemic clamps with measures of basal and steady state glucose turnover. Exclusion criteria included history of hypertension, previous insulin use and positive anti-pancreatic islet cell antibodies. Linear regression analysis was used to evaluate relationships between HOMA (dependent variable) and four direct measures of insulin sensitivity: (a) MINIMOD SI, (b) clamps steady state (SS) glucose requirements (GINF), (c) clamp ratio of SS/GINF: SS plasma insulin (GINF: ins), and (d) clamp SS glucose clearance rate. RESULTS: During pregnancy, the HOMA calculations were significantly but weakly correlated with MINIMOD SI (R = 0.34, R 2 = 0.12); clamp GINF (R = 0.454, R =0.21), clamp SS GINF: insulin (R = 0.377, R = 0.142) and clamp SS glucose clearance (R = 0.404, R = 0.163). Raw data demonstrate non-linear relationship between HOMA and direct measures of insulin sensitivity in pregnant women. CONCLUSION: The HOMA, an empirical calculation based on fasting insulin has a non-linear relationship to insulin sensitivity. During pregnancy the range of insulin sensitivity is truncated compared to non-pregnant individuals thus limiting the applicability of HOMA as a measure of insulin sensitivity in pregnancy.

Impaired insulin sensitivity is an independent predictor of common carotid intima-media thickness in a population sample of elderly men

Most previous studies of associations between insulin sensitivity and common carotid artery (CCA) atherosclerosis have been conducted in small samples, have not used direct measurement of insulin sensitivity, and have yielded inconclusive results. We investigated associations of CCA intima-media thickness (IMT) and diameter (CCA-D) measured by B-mode ultrasound and insulin sensitivity measured by the euglycemic hyperinsulinemic clamp test together with risk factors of the insulin resistance syndrome in a community-based sample of 493 elderly men. The clamp glucose disposal rate was an independent predictor of CCA-IMT in multivariate models adjusting for blood pressure, smoking, serum cholesterol, and body mass index (1% decrease in CCA-IMT for a 1 unit increase in glucose disposal rate, P=0.009). Glucose disposal rate was significantly related to CCA-D in univariate ( r=−0.11, P=0.02) but not in multivariate models. In conclusion, this study is the first to establish impaired insulin sensitivity, measured by the euglycemic hyperinsulinemic clamp test, as an independent predictor of CCA-IMT in a population-based sample of elderly men.

Minimal model-based insulin sensitivity has greater heritability and a different genetic basis than homeostasis model assessment or fasting insulin.

Insulin resistance is an important risk factor for development of type 2 diabetes as well as other chronic conditions, including hypertension, cardiovascular disease, and colon cancer. To find genes for insulin resistance it is necessary to assess insulin action in large populations. We have previously measured insulin action in a large cohort of subjects (Insulin Resistance and Atherosclerosis Study [IRAS] Family Study) using the minimal model approach. In this study, we compare sensitivity from the minimal model (insulin sensitivity index [S(I)]) with the measure of insulin resistance emanating from the homeostasis model assessment (HOMA) approach. The former measure emerges from the glycemic response to endogenous and exogenous insulin; the latter is based solely on fasting measures of glucose and insulin. A total of 112 pedigrees were represented, including 1,362 individuals with full phenotypic assessment. Heritability of S(I) was significantly greater than that for HOMA (0.310 vs. 0.163) and for fasting insulin (0.171), adjusted for age, sex, ethnicity, and BMI. In addition, correlation between S(I) and either HOMA or fasting insulin was only approximately 50% accounted for by genetic factors, with the remainder accounted for by environment. Thus S(I), a direct measure of insulin sensitivity, is determined more by genetic factors rather than measures such as HOMA, which reflect fasting insulin.