Tracey McLaughlin

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

B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies

Chronic inflammation characterized by T cell and macrophage infiltration of visceral adipose tissue (VAT) is a hallmark of obesity-associated insulin resistance and glucose intolerance. Here we show a fundamental pathogenic role for B cells in the development of these metabolic abnormalities. B cells accumulate in VAT in diet-induced obese (DIO) mice, and DIO mice lacking B cells are protected from disease despite weight gain. B cell effects on glucose metabolism are mechanistically linked to the activation of proinflammatory macrophages and T cells and to the production of pathogenic IgG antibodies. Treatment with a B cell–depleting CD20 antibody attenuates disease, whereas transfer of IgG from DIO mice rapidly induces insulin resistance and glucose intolerance. Moreover, insulin resistance in obese humans is associated with a unique profile of IgG autoantibodies. These results establish the importance of B cells and adaptive immunity in insulin resistance and suggest new diagnostic and therapeutic modalities for managing the disease.

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

Enhanced proportion of small adipose cells in insulin-resistant vs insulin-sensitive obese individuals implicates impaired adipogenesis

Aims/hypothesisThe biological mechanism by which obesity predisposes to insulin resistance is unclear. One hypothesis is that larger adipose cells disturb metabolism via increased lipolysis. While studies have demonstrated that cell size increases in proportion to BMI, it has not been clearly shown that adipose cell size, independent of BMI, is associated with insulin resistance. The aim of this study was to test this widely held assumption by comparing adipose cell size distribution in 28 equally obese, otherwise healthy individuals who represented extreme ends of the spectrum of insulin sensitivity, as defined by the modified insulin suppression test.Subjects and methodsSubcutaneous periumbilical adipose tissue biopsy samples were fixed in osmium tetroxide and passed through the Beckman Coulter Multisizer to obtain cell size distributions. Insulin sensitivity was quantified by the modified insulin suppression test. Quantitative real-time PCR for adipose cell differentiation genes was performed for 11 subjects.ResultsAll individuals exhibited a bimodal cell size distribution. Contrary to expectations, the mean diameter of the larger cells was not significantly different between the insulin-sensitive and insulin-resistant individuals. Moreover, insulin resistance was associated with a higher ratio of small to large cells (1.66 ± 1.03 vs 0.94 ± 0.50, p = 0.01). Similar cell size distributions were observed for isolated adipose cells. The real-time PCR results showed two- to threefold lower expression of genes encoding markers of adipose cell differentiation (peroxisome proliferator-activated receptor γ1 [PPARγ1], PPARγ2, GLUT4, adiponectin, sterol receptor element binding protein 1c) in insulin-resistant compared with insulin-sensitive individuals.Conclusions/interpretationThese results suggest that after controlling for obesity, insulin resistance is associated with an expanded population of small adipose cells and decreased expression of differentiation markers, suggesting that impairment in adipose cell differentiation may contribute to obesity-associated insulin resistance.

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.

Reversible Hyperinsulinemic Hypoglycemia after Gastric Bypass: A Consequence of Altered Nutrient Delivery

CONTEXT Severe hypoglycemia after Roux-en-Y gastric bypass surgery (RYGB) is an increasingly recognized condition, characterized by neuroglycopenia and inappropriately elevated insulin concentrations that occur primarily in the postprandial state. Both pathophysiology and treatment of this disorder remain elusive, but it has been postulated that hyperplasia and/or hypertrophy of beta-cells due to morbid obesity and/or postsurgical nesidioblastosis may contribute. OBJECTIVE The objective of this study was to elucidate the pathophysiology of this condition; specifically, we hypothesized that metabolic abnormalities were a function of altered nutrient transit through the gastrointestinal tract rather than anatomical changes to pancreatic beta-cells that would lead to consistently high insulin secretion irrespective of nutrient transit route. DESIGN/SETTING/SUBJECT/OUTCOME MEASURES: We describe a unique case wherein gastrostomy tube (GT) insertion into the remnant stomach reversed neuroglycopenic symptoms. This subject was admitted to a university hospital research center for standardized measurement of glucose, insulin, and incretin hormones including glucagon-like peptide-1, gastric-inhibitory peptide, and glucagon. RESULTS Standardized liquid meal administration via GT vs. oral route demonstrated complete reversal of severe metabolic abnormalities that included hypersecretion of insulin and GLP-1. CONCLUSION Post-RYGB hyperinsulinemia and hypoglycemia result entirely from altered nutrient delivery rather than generalized hyperfunction of beta-cells due to presurgical hypertrophy/hyperfunction or postsurgical nesidioblastosis. These findings support the use of GT for treatment of severe cases and have implications for surgical manipulations that may reverse/prevent this condition.

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.

T-Cell Profile in Adipose Tissue Is Associated With Insulin Resistance and Systemic Inflammation in Humans

Objective— The biological mechanisms linking obesity to insulin resistance have not been fully elucidated. We have shown that insulin resistance or glucose intolerance in diet-induced obese mice is related to a shift in the ratio of pro- and anti-inflammatory T cells in adipose tissue. We sought to test the hypothesis that the balance of T-cell phenotypes would be similarly related to insulin resistance in human obesity. Approach and Results— Healthy overweight or obese human subjects underwent adipose-tissue biopsies and quantification of insulin-mediated glucose disposal by the modified insulin suppression test. T-cell subsets were quantified by flow cytometry in visceral (VAT) and subcutaneous adipose tissue (SAT). Results showed that CD4 and CD8 T cells infiltrate both depots, with proinflammatory T-helper (Th)-1, Th17, and CD8 T cells, significantly more frequent in VAT as compared with SAT. T-cell profiles in SAT and VAT correlated significantly with one another and with peripheral blood. Th1 frequency in SAT and VAT correlated directly, whereas Th2 frequency in VAT correlated inversely, with plasma high-sensitivity C-reactive protein concentrations. Th2 in both depots and peripheral blood was inversely associated with systemic insulin resistance. Furthermore, Th1 in SAT correlated with plasma interleukin-6. Relative expression of associated cytokines, measured by real-time polymerase chain reaction, reflected flow cytometry results. Most notably, adipose tissue expression of anti-inflammatory interleukin-10 was inversely associated with insulin resistance. Conclusions— CD4 and CD8 T cells populate human adipose tissue and the relative frequency of Th1 and Th2 are highly associated with systemic inflammation and insulin resistance. These findings point to the adaptive immune system as a potential mediator between obesity and insulin resistance or inflammation. Identification of antigenic stimuli in adipose tissue may yield novel targets for treatment of obesity-associated metabolic disease.