Fahim Abbasi

Use of Metabolic Markers To Identify Overweight Individuals Who Are Insulin Resistant

Context Insulin resistance is associated with adverse outcomes, such as cardiovascular disease and type 2 diabetes mellitus. The insulin suppression test, the gold standard method of diagnosing insulin resistance, is cumbersome to administer. A simple method to identify persons with insulin resistance would be useful. Contribution In a group of overweight individuals, 3 easily measured variables (triglyceride levels, the ratio of triglyceride to high density lipoprotein [HDL] cholesterol levels, and insulin concentration) identified insulin-resistant individuals with sensitivities of 57% to 67% and specificities of 68% to 85%. Implications Triglyceride levels, the triglyceride-HDL cholesterol ratio, and insulin concentration are imperfect but practical methods for identifying overweight persons who are insulin resistant and at greatest risk for complications. The Editors Recent reports (1) indicate that more than 50% of the U.S. population is overweight (body mass index [BMI] 25 kg/m2), with approximately 20% designated as obese (BMI 30 kg/m2). Because overweight is important in the genesis of type 2 diabetes mellitus and cardiovascular disease (CVD), the absolute number of Americans in this category is disturbing. The gravity of the problem is accentuated in light of the report that only approximately 50% of physicians polled provided weight loss counseling (2) and that pharmacologic treatment of weight loss is not being used appropriately in overweight persons (3). Reluctance to assign weight control programs a high priority might be decreased if identifying overweight or obese individuals at greatest risk for adverse health consequences were possible, particularly if weight loss would significantly attenuate the risk. In this context, it is necessary to begin by emphasizing that the prevalence of insulin resistance is increased in patients with type 2 diabetes mellitus, essential hypertension, and CVD and that insulin resistance and compensatory hyperinsulinemia have been shown to be independent predictors of all 3 clinical syndromes (4-9). Since obese individuals tend to be insulin resistant and become more insulin sensitive with weight loss (10), an obvious approach to identify individuals who would most benefit from weight loss is to measure insulin-mediated glucose disposal. However, direct measures of insulin-mediated glucose disposal are not clinically practical. On the other hand, overweight persons are also at increased risk for glucose intolerance, and the higher the plasma glucose or insulin concentrations in nondiabetic persons, the more likely that the persons are insulin resistant (4, 11). Thus, differences in fasting plasma glucose or insulin concentrations might be useful to identify insulin-resistant persons. These persons also have a characteristic dyslidemia (4), and measuring these variables might also help identify insulin resistance. For example, plasma triglyceride and high-density lipoprotein (HDL) cholesterol levels are independently associated with insulin resistance (12) and are independent predictors of CVD (13, 14). In addition, the plasma concentration ratio of total cholesterol to HDL cholesterol is well recognized as a predictor of CVD (15) and is also highly correlated with insulin resistance (16). A less commonly considered CVD risk factor is the ratio of triglyceride to HDL cholesterol, despite the observation that the triglycerideHDL cholesterol ratio is as significant a predictor of CVD as are the ratios of low-density lipoprotein (LDL) cholesterol to HDL cholesterol or total cholesterol to HDL cholesterol (17). A more recent study showed that persons in the highest tertile of the triglycerideHDL cholesterol ratio had increased CVD risk in the absence of the 4 conventional risk factors, whereas those in the lowest tertile had decreased risk in the presence of the same 4 risk factors (18). Although obese individuals tend to be insulin resistant, hyperinsulinemic, glucose intolerant, and dyslipidemic, not all overweight or obese individuals are insulin resistant, nor do they all have the characteristic disturbances in glucose or lipid metabolism (19-23). Furthermore, not all CVD risk factors improve with weight loss, and the metabolic benefits associated with weight loss are largely confined to overweight or obese individuals with these abnormalities at baseline (20-23). Given the relative ease of measuring plasma glucose, insulin, and lipid concentrations, and their importance as both CVD risk factors and manifestations of insulin resistance, we attempted to develop a simple clinical approach using these measurements to identify overweight or obese individuals who are both insulin resistant and at greatest risk for CVD. Methods The study sample consisted of 258 persons with a BMI of 25 kg/m2 or greater, classified as overweight or obese by National Institutes of Health (24) and World Health Organization criteria (25). Participants were drawn from a large database of 490 healthy volunteers who have participated in research studies in the past 10 years. These studies typically used newspaper advertisements to identify persons without known disease to participate in our efforts to define the relationship between insulin resistance and metabolic abnormalities. According to their medical histories, study participants did not have major chronic medical illnesses, including CVD, and were not taking any medication known to influence insulin resistance or lipid metabolism (such as corticosteroids and lipid-lowering drugs). No clinically significant abnormalities were found during physical examination; participants were not anemic, had normal liver and kidney function, and were nondiabetic on the basis of plasma glucose concentrations in response to a standard oral glucose challenge (26). The 258 individuals included 127 men and 131 women with a mean age (SD) of 50 16 years (range, 19 to 70 years) and a mean BMI (SD) of 29.2 3.2 kg/m2 (range, 25.0 to 39.1 kg/m2). Most participants were white (87%); the remaining participants were Asian American (9%), Hispanic (3%), or African American (1%). Insulin-mediated glucose disposal was estimated by a modification (27) of the insulin suppression test introduced and validated by our research group (28, 29). We have used this approach for more than 35 years to measure insulin action, and results are highly correlated (r > 0.9) with the more commonly used euglycemic, hyperinsulinemic clamp approach (29). After an overnight fast, intravenous catheters are placed in each of the patients arms. A 180-minute infusion of somatostatin (250 g/h), insulin (179 mol/m2 per min 1), and glucose (13.3 mmol/m 2 2 per min) is administered into 1 arm. Blood samples are collected from the other arm every 30 minutes initially and at 10-minute intervals from 150 to 180 minutes of the infusion to determine the steady-state plasma insulin and glucose concentrations. Since steady-state plasma insulin concentrations are similar for all participants, the steady-state plasma glucose concentration directly measures the insulins ability to mediate disposal of the infused glucose load; the higher the steady-state plasma glucose concentration, the more insulin resistant the patient. Blood samples were obtained before the insulin suppression test to measure plasma glucose (30), insulin (31), and lipid and lipoprotein (32-34) levels by methods that were identical during the period of study. We have found that insulins ability to stimulate glucose disposal varied continuously in a sample of 490 healthy persons (35), precluding an objective definition of an individual as being insulin sensitive or insulin resistant. However, in 2 prospective studies (8, 9), we showed that CVD and glucose intolerance or type 2 diabetes developed to a statistically significantly greater degree in one third of the healthy sample that was the most insulin resistant (that is, the tertile with the highest steady-state plasma glucose concentrations). On the basis of these considerations and for the purposes of this analysis, we used as an operational definition of insulin resistance a steady-state plasma glucose concentration in the upper tertile of the distribution of the original 490 healthy volunteers. Because of possible interaction between metabolic markers, sex, and menopausal status of women, we performed logistic regression analysis for predicting insulin resistance that included the best metabolic marker, sex, menopausal status, and all interaction terms. Since there were no significant interactions, men and women, regardless of their menopausal status, were considered together in subsequent analyses. Clinical utility of metabolic markers to identify individuals in the most insulin-resistant tertile was evaluated by constructing receiver-operating characteristic (ROC) curves, which depict the relationship between true-positive (sensitivity) and false-positive (1 specificity) test results for each diagnostic marker. Markers for which a relative increase in sensitivity is matched by a similar increase in false-positive results are represented by a diagonal line and are of less clinical use. Metabolic markers considered were fasting plasma concentrations of glucose, insulin, triglyceride, cholesterol, and HDL cholesterol, as well as the cholesterolHDL cholesterol ratio and the triglycerideHDL cholesterol ratio. Areas under the ROC curves were compared using the method of Hanley and McNeil (36). The metabolic markers of insulin resistance that were statistically significantly better performers were selected for cut-point analysis to identify specific values that would be useful in predicting insulin resistance. The cut-points diagnostic of the top tertile of steady-state plasma glucose were based on the formula M = ws + (1 w) p, where w = prevalence of disease (top tertile steady-state plasma glucose), s = sensitivity, and p = specificity (37). According to this equation, the cut-point identi

Relationship between obesity, insulin resistance, and coronary heart disease risk.

OBJECTIVES The study goals were to: 1) define the relationship between body mass index (BMI) and insulin resistance in 314 nondiabetic, normotensive, healthy volunteers; and 2) determine the relationship between each of these two variables and coronary heart disease (CHD) risk factors. BACKGROUND The importance of obesity as a risk factor for type 2 diabetes and hypertension is well-recognized, but its role as a CHD risk factor in nondiabetic, normotensive individuals is less well established. METHODS Insulin resistance was quantified by determining the steady-state plasma glucose (SSPG) concentration during the last 30 min of a 180-min infusion of octreotide, glucose, and insulin. In addition, nine CHD risk factors: age, systolic blood pressure, diastolic blood pressure (DBP), total cholesterol, triglycerides (TG), high-density lipoprotein (HDL) cholesterol and low-density lipoprotein cholesterol concentrations, and glucose and insulin responses to a 75-g oral glucose load were measured in the volunteers. RESULTS The BMI and the SSPG concentration were significantly related (r = 0.465, p < 0.001). The BMI and SSPG were both independently associated with each of the nine risk factors. In multiple regression analysis, SSPG concentration added modest to substantial power to BMI with regard to the prediction of DBP, HDL cholesterol and TG concentrations, and the glucose and insulin responses. CONCLUSIONS Obesity and insulin resistance are both powerful predictors of CHD risk, and insulin resistance at any given degree of obesity accentuates the risk of CHD and type 2 diabetes.

Differentiation Between Obesity and Insulin Resistance in the Association With C-Reactive Protein

Background—Plasma C-reactive protein (CRP) concentrations are increased in obese and/or hyperinsulinemic individuals. The goal of this study was to determine if the relation between insulin resistance and CRP was independent of obesity. Methods and Results—Plasma CRP concentrations were measured before and after 3 months of calorie restriction in 38 healthy, obese women. Steady-state plasma glucose (SSPG) concentration during a 180-minute infusion of octreotide, glucose, and insulin was used to stratify participants into insulin-resistant (IR, n=20) or insulin-sensitive (n=18) groups, similar in terms of mean age (46±2 versus 44±2 years), body mass index (32.0±0.4 versus 31.4±0.3 kg/m2), and waist circumference (96±2 versus 95±2 cm). Mean CRP (0.39±0.08 versus 0.12±0.03 mg/dL, P =0.003) concentrations were higher in the IR group, as were day-long plasma glucose and insulin responses (P <0.001). There was a significant correlation at baseline between CRP and day-long plasma integrated insulin response (r =0.47, P =0.001) but not between CRP and body mass index (r =0.14) or waist circumference (r =0.10). Weight loss was similar in the two groups ( 8.7±0.9 versus 8.4±0.8 kg) but was associated with significant (P <0.001) decreases in SSPG and CRP concentrations in the IR group only. Regression analysis showed that SSPG and day-long plasma insulin response were the only significant predictors of CRP concentration. Conclusions—CRP concentrations are elevated predominantly in obese individuals who are also insulin resistant and fall in parallel with weight loss–associated improvements in insulin resistance. The relation between CRP concentrations and insulin resistance is independent of obesity.

Plasma concentrations of asymmetric dimethylarginine are increased in patients with type 2 diabetes mellitus.

C heart disease (CHD), the major cause of morbidity and mortality in patients with type 2 diabetes, cannot be entirely explained by the presence of conventional risk factors. Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor of nitric oxide synthase. Plasma ADMA concentrations have been shown to be elevated in animals and patients with hypercholesterolemia and atherosclerosis, and intracellular concentrations of ADMA are increased in regenerated endothelial cells after balloon injury in rabbits with alloxan-induced hyperglycemia. Thus, we hypothesized that increased plasma concentrations of ADMA, by inhibiting NO synthase, could play a role in the depressed endothelial cell-dependent vasodilator responses that have been described in patients with type 2 diabetes. Because endothelial dysfunction is an early event in the process of atherogenesis, we also hypothesized that plasma ADMA concentrations are elevated in hyperglycemic patients with type 2 diabetes, and could contribute to the accelerated atherogenesis in these persons. To begin evaluation of these hypotheses, we compared plasma ADMA concentrations in normal volunteers with those in patients with type 2 diabetes. • • • The study was approved by the Stanford Human Subjects Committee, and volunteers gave informed consent before entering the clinical research center. The study group consisted of 18 nondiabetic subjects and 16 patients with type 2 diabetes. No patient with type 2 diabetes had received any pharmacologic treatment for type 2 diabetes within the past 4 weeks, and had no apparent diabetic complication. All participants had a normal physical examination, blood count, and chemical screening battery. Blood was drawn after an overnight fast, and plasma frozen at –70°C until thawed for measurement of plasma glucose and lipid concentrations as described previously. Plasma concentrations of ADMA and symmetric dimethylarginine (SDMA) in plasma were measured by high-performance liquid chromatography with precolumn derivatization with o-phthaldialdehyde using a modification of a previously described method. ADMA concentrations were calculated by comparing the ADMA/homoarginine ratio with standards of known concentrations. The recovery rate for ADMA was 85% and the intrasample variation was 4%. The detection limit of the assay was 0.1 M. Results are expressed as mean SE, and the statistical significance of differences between the 2 groups estimated by Student’s t test. Results in Table 1 show that the 2 groups to be compared were similar in terms of age, gender distribution, body mass index, and total and low-density lipoprotein cholesterol concentrations. By selection, plasma glucose concentrations were significantly (p 0.001) higher in patients with type 2 diabetes. In addition, plasma triglyceride concentrations were higher (p 0.02) and high-density lipoprotein cholesterol concentrations lower (p 0.005) in patients with type 2 diabetes. Importantly, low-density lipoprotein cholesterol concentrations were similar in the first 2 groups. Figure 1 shows the individual and mean ADMA concentrations of the 2 groups, and it can be seen that the ADMA concentrations were significantly higher (p 0. 01) in patients with type 2 diabetes (1.59 0.22 vs 0.69 0.04 mol/L, p 0.001). Also, the separation of the 2 groups was almost complete, with 14 of the 16 patients with type 2 diabetes having ADMA concentrations higher than all 18 normal volunteers. From the Stanford University School of Medicine, Stanford, California. This report was supported by Research Grants HL-08506, HL-58638, and RR-00070 from the National Institutes of Health, Bethesda, Maryland. Dr. Reaven’s address is: Division of Cardiovascular Medicine, Falk CVRC, Stanford Medical Center, 300 Pasteur Drive, Stanford, California 94305. E-mail: greaven@cvmed.stanford.edu. Manuscript received April 30, 2001; revised manuscript received and accepted July 17, 2001. TABLE 1 Baseline Characteristics of Normal Volunteers and Patients With Type 2 Diabetes

Preferential Fat Deposition in Subcutaneous Versus Visceral Depots Is Associated with Insulin Sensitivity

BACKGROUND Studies on the relationship between regional fat and insulin resistance yield mixed results. Our objective was to determine whether regional fat distribution, independent of obesity, is associated with insulin resistance. DESIGN Subjects included 115 healthy, overweight/moderately obese adults with body mass index (BMI) 25-36.9 kg/m(2) who met predetermined criteria for being insulin resistant (IR) or insulin sensitive (IS) based on the modified insulin suppression test. Computerized tomography was used to quantify visceral adipose tissue (VAT), sc adipose tissue (SAT), and thigh adipose tissue. Fat mass in each depot was compared according to IR/IS group, adjusting for BMI and sex. RESULTS Despite nearly identical mean BMI in the IR vs. IS groups, VAT and %VAT were significantly higher in the IR group, whereas SAT, %SAT, and thigh sc fat were significantly lower. In logistic regression analysis, each sd increase in VAT increased the odds of being IR by 80%, whereas each increase in SAT decreased the odds by 48%; each increase in thigh fat decreased the odds by 59% and retained significance after adjusting for other depots. When grouped by VAT tertile, IS vs. IR individuals had significantly more SAT. There was no statistically significant interaction between sex and these relationships. CONCLUSION These data demonstrate that after adjustment for BMI and VAT mass, sc abdominal and thigh fat are protective for insulin resistance, whereas VAT, after adjustment for SAT and BMI, has the opposite effect. Whether causal in nature or a marker of underlying pathology, these results clarify that regional distribution of fat-favoring sc depots is associated with lower risk for insulin resistance.

Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

High carbohydrate diets, triglyceride-rich lipoproteins, and coronary heart disease risk.

In this study we compared the effects of variations in dietary fat and carbohydrate (CHO) content on concentrations of triglyceride-rich lipoproteins in 8, healthy, nondiabetic volunteers. The diets contained, as a percentage of total calories, either 60% CHO, 25% fat, and 15% protein, or 40% CHO, 45% fat, and 15% protein. They were consumed in random order for 2 weeks, with a 2-week washout period in between. Measurements were obtained at the end of each dietary period of plasma triglyceride, cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, remnant lipoprotein (RLP) cholesterol, and RLP triglyceride concentrations, both after an overnight fast and throughout an 8-hour period (8 A.M. to 4 P.M.) in response to breakfast and lunch. The 60% CHO diet resulted in higher (mean +/- SEM) fasting plasma triglycerides (206 +/- 50 vs 113 +/- 19 mg/dl, p = 0.03), RLP cholesterol (15 +/- 6 vs 6 +/- 1 mg/dl, p = 0.005), RLP triglyceride (56 +/- 25 vs 16 +/- 3 mg/dl, p = 0.003), and lower HDL cholesterol (39 +/- 3 vs 44 +/- 3 mg/dl, p = 0.003) concentrations, without any change in LDL cholesterol concentration. Furthermore, the changes in plasma triglyceride, RLP cholesterol, and RLP triglyceride persisted throughout the day in response to breakfast and lunch. These results indicate that the effects of lowfat diets on lipoprotein metabolism are not limited to higher fasting plasma triglyceride and lower HDL cholesterol concentrations, but also include a persistent elevation in RLPs. Given the atherogenic potential of these changes in lipoprotein metabolism, it seems appropriate to question the wisdom of recommending that all Americans should replace dietary saturated fat with CHO.

Plasma homocysteine concentrations in healthy volunteers are not related to differences in insulin-mediated glucose disposal

This study was initiated to test the hypothesis that plasma homocysteine concentrations are increased in insulin resistant individuals. For this purpose, the relationship between insulin resistance, as assessed by the steady-state plasma glucose (SSPG) concentration during the insulin suppression test, and fasting plasma homocysteine concentration was defined in 55 healthy volunteers. The results indicated that homocysteine concentrations did not vary as a function of SSPG concentrations (r = 0.02, P = 0.88). Furthermore, mean (+/- S.E.M.) plasma homocysteine concentrations were similar (8.2+/-0.4 vs. 8.7+/-0.7 micromol/l) in individuals classified as being either insulin sensitive (SSPG <100 mg/dl) or insulin resistant (SSPG >180 mg/dl). On the other hand, SSPG concentration was significantly correlated with fasting plasma insulin (r = 0.58, P<0.001), triglycerides (r = 0.34, P<0.05), and HDL-cholesterol (r = -0.36, P = 0.04) concentrations. These data strongly suggest that the increased risk of atherosclerosis associated with increased plasma homocysteine concentrations is unrelated to insulin resistance and/or the metabolic abnormalities associated with it.

Prevalence of Insulin Resistance and Related Risk Factors for Cardiovascular Disease in Patients With Essential Hypertension

BACKGROUND There is evidence that the subgroup of patients with essential hypertension who are also insulin resistant is at increased risk of cardiovascular disease (CVD). We are unaware of the frequency of insulin resistance in patients with essential hypertension as well as the CVD risk in this subgroup of patients. This analysis was aimed at providing the prevalence of insulin resistance and associated CVD risk factors in treated and untreated patients with essential hypertension. METHODS The study population consisted of 126 patients with hypertension: 56 untreated and 70 in a stable treatment program. Body mass index (BMI), blood pressure, plasma glucose and insulin responses to an oral glucose challenge, lipid and lipoprotein concentrations, and steady-state plasma glucose (SSPG) concentration during the insulin suppression test were measured. Insulin resistance was defined operationally as a SSPG concentration >180 mg/dl. RESULTS Demographic characteristics and metabolic CVD risk factors were comparable in both groups, with 30-50% of both treated and untreated patients having abnormalities of all risk factors measured. Approximately 50% of patients met the criteria for insulin resistance in both groups, and the prevalence of abnormal CVD risk factors in this group was increased two to threefold as compared to the other half of the subjects. CONCLUSIONS Approximately 50% of patients with essential hypertension, both treated and untreated, appear to be insulin resistant, and CVD risk factors are greatly accentuated in this subset of patients.

Comparison of Waist Circumference Versus Body Mass Index in Diagnosing Metabolic Syndrome and Identifying Apparently Healthy Subjects at Increased Risk of Cardiovascular Disease

The goal of this study was to compare the impact of differences in waist circumference (WC) defined according to the International Diabetes Federation (IDF) and the Adult Treatment Panel III (ATP III) and body mass index (BMI) on cardiovascular disease risk factors in 402 apparently healthy volunteers of European ancestry. Consequently, measurements were made of the WC, BMI, blood pressure, glucose, and lipid components of metabolic syndrome (MS) and insulin-mediated glucose uptake. Subjects were divided according to WC (IDF and ATP III criteria) and by normal weight, overweight, or obesity using BMI, and comparisons were made of the effect of these different indexes of adiposity on cardiovascular disease risk factors. The results indicated that WC and BMI significantly correlated (p <0.001) and were associated with differences in insulin-mediated glucose uptake to a similar degree in men (r = 0.57 and r = 0.59) and women (r = 0.53 and r = 0.52). Prevalences of MS were essentially identical irrespective of the measure of WC used (ATP III vs IDF), as were metabolic characteristics of those classified using IDF or ATP III criteria. Cardiovascular disease risk factor status did not vary substantially when subjects were divided on the basis of WC or BMI. In conclusion, prevalences of MS or cardiovascular disease risk factors did not vary as a function of differences in IDF and ATP III criteria for WC. BMI identified individuals at increased cardiovascular disease risk as effectively as determination of WC.