Relationship between surrogate estimates and direct measurement of insulin resistance in women with polycystic ovary syndrome.

PURPOSE: To evaluate the relationship between surrogate estimates of insulin resistance and a direct measurement of insulin-mediated glucose uptake women with and without PCOS. METHODS: Retrospective cohort study of 75 PCOS and 118 controls. Fasting plasma glucose and insulin concentrations, insulin resistance as determined by the insulin suppression test, calculation of multiple surrogate estimates of insulin resistance, total and free testosterone concentrations, and correlations between the direct measure and surrogate estimates of insulin resistance were evaluated. RESULT(S): Surrogate markers of insulin resistance were correlated to a variable, but statistically significant degree with the direct measure of insulin resistance in control population and the women with PCOS. There was no correlation between the surrogate estimates of insulin resistance and total or free plasma testosterone concentrations. CONCLUSION(S): The surrogate estimates of insulin resistance evaluated were significantly related to a direct measure of insulin resistance, and this was true of both the control population and women with PCOS. The magnitude of the relationship between the surrogate estimates and the direct measurement was comparable and not significantly altered by androgen levels. Fasting plasma insulin concentration seems to be at least as accurate as any other surrogate estimate, and is by far the simplest.

Comparison of two surrogate estimates of insulin resistance to predict cardiovascular disease in apparently healthy individuals.

BACKGROUND AND AIMS: Insulin resistance is associated with a cluster of abnormalities that increase cardiovascular disease (CVD). Several indices have been proposed to identify individuals who are insulin resistant, and thereby at increased CVD risk. The aim of this study was to compare the abilities of 3 indices to accomplish that goal: 1) plasma triglyceride × glucose index (TG × G); 2) plasma triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-C); and 3) Metabolic Syndrome (MetS). METHODS AND RESULTS: In a population sample of 723 individuals (486 women and 237 men, 50 ± 16 and 51 ± 16 years old, respectively), baseline demographic and metabolic variables known to increase CVD risk and incident CVD were compared among individuals defined as high vs. low risk by: TG × G; TG/HDL-C; or MetS. CVD risk profiles appeared comparable in high risk subjects, irrespective of criteria. Crude incidence of CVD events was increased in high risk subjects: 12.2 vs. 5.3% subjects/10 years, p = 0.005 defined by TG/HDL-C; 13.4 vs. 5.3% subjects/10 years, p = 0.002 defined by TG × G; and 13.4% vs. 4.5% of subjects/10 years, p < 0.001 in subjects with the MetS. The area under the ROC curves to predict CVD were similar, 0.66 vs. 0.67 for TG/HDL-C and TG × G, respectively. However, when adjusted by age, sex and multiple covariates, hazard ratios for incident CVD were significantly increased in high risk patients classified by either TG/HDL-C ratio (2.18, p = 0.021) or MetS (1.93, p = 0.037), but not by TG × G index (1.72, p = 0.087). CONCLUSION: Although the 3 indices identify CVD risk comparably, the TG × G index seems somewhat less effective at predicting CVD.

Use of the triglyceride/high-density lipoprotein cholesterol ratio to identify cardiometabolic risk: impact of obesity?

There is evidence that the plasma concentration ratio of triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C) identifies insulin resistance and increased cardiometabolic risk and outcome in apparently healthy individuals. Since use of the TG/HDL-C ratio to accomplish this task in persons over a wide range of adiposity has not been studied, the ability of previously defined sex-specific TG/HDL-C cut-points to identify increased cardiometabolic risk was evaluated in apparently healthy normal weight, overweight, and obese individuals. Data were analyzed from a population-based study of apparently healthy men (n=416) and women (n=893), subdivided into categories by body mass index (BMI, kg/m2): normal weight (BMI 20.0-24.9), overweight (BMI 25.0-29.9) and obese (BMI 30.0-34.9). The adiposity groups were further stratified on the basis of their TG/HDL-C ratio into groups defined as being either at 'high risk' versus 'low risk' of cardiometabolic disease. Multiple cardiometabolic risk factors were compared between these subgroups, as was their degree of insulin resistance assessed by fasting plasma insulin concentration and homeostasis model assessment of insulin resistance. The proportion of high-risk individuals varied with BMI category, ranging from 14% (normal weight) to 36% (obese). However, within each BMI category high-risk individuals had a significantly more adverse cardiometabolic risk profile. Finally, the adjusted OR of being insulin resistant was significantly greater in those with a high TG/HDL-C ratio in the normal (3.02), overweight (2.86), and obese (2.51) groups. Thus, irrespective of differences in BMI, the TG/HDL-C ratio identified apparently healthy persons with a more adverse cardiometabolic risk profile associated with an increased prevalence of insulin resistance.

Relationship between insulin sensitivity and insulin secretion rate: not necessarily hyperbolic.

AIMS: There is general acceptance that the physiological relationship between insulin sensitivity and insulin secretion is hyperbolic. This conclusion has evolved from studies in which one test assessed both variables, and changes in plasma insulin concentration were used as a surrogate measure for insulin secretion rate. The aim of this study was to see if a hyperbolic relationship would also emerge when separate and direct measures were used to quantify both insulin sensitivity and insulin secretion rate. METHODS: Steady-state plasma glucose (SSPG) was determined in 146 individuals without diabetes using the insulin suppression test, with 1/SSPG used to quantify insulin sensitivity. The graded-glucose infusion test was used to quantify insulin secretion rate. Plasma glucose and insulin concentrations obtained during an oral glucose tolerance test (OGTT) were used to calculate surrogate estimates of insulin action and insulin secretion rate. A hyperbolic relationship was assumed if the ß coefficient was near -1 using the following model: log (insulin secretion measure) = constant + ß × log (insulin sensitivity measure). RESULTS: OGTT calculations of insulin sensitivity (Matsuda) and plasma insulin response [ratio of insulin/glucose area-under-the-curve (AUC) or insulin total AUC] provided the expected hyperbolic relationship [ß = -0.95, 95% CI (-1.09, -0.82); -1.06 (-1.14, -0.98)]. Direct measurements of insulin sensitivity and insulin secretion rate did not yield the same curvilinear relationship [ß = -1.97 (-3.19, -1.36)]. CONCLUSIONS: These findings demonstrate that the physiological relationship between insulin sensitivity and insulin secretion rate is not necessarily hyperbolic, but will vary with the method(s) by which it is determined.

Plasma triglyceride determination can identify increased risk of statin-induced type 2 diabetes: a hypothesis.

OBJECTIVE: To evaluate the ability of plasma triglyceride (TG) measurements to identify statin-treated persons at accentuated risk of statin-induced type 2 diabetes (T2DM). METHODS: The experimental population consisted of nondiabetic, statin-treated patients (n = 469), classified as being at high risk for T2DM, subdivided on the basis of a plasma TG concentration of 1.7 mmol/L. Comparisons were made of demographic characteristics, concentrations of fasting glucose, insulin, HbA1c, and hs-CRP, oral glucose tolerance tests, estimates of insulin action and secretion, and lipid/lipoprotein profiles. RESULTS: Despite similar fasting glucose and HbA1c concentrations, patients with elevated TG concentrations displayed markers of increased risk of T2DM (insulin resistance and compensatory hyperinsulinemia), more adverse lipid/lipoprotein profiles, and increased prevalence of abnormal hs-CRP values. CONCLUSION: These findings demonstrate that plasma TG concentrations ≥ 1.7 mmol/L identified a subset of individuals at enhanced risk of developing statin-induced diabetes within a population classified prior to statin treatment as being at high risk of T2DM.

Effect of liraglutide administration and a calorie-restricted diet on lipoprotein profile in overweight/obese persons with prediabetes.

BACKGROUND AND AIMS: To evaluate the effects of 14 weeks of liraglutide plus modest caloric restriction on lipid/lipoprotein metabolism in overweight/obese persons with prediabetes. METHODS AND RESULTS: Volunteers with prediabetes followed a calorie-restricted diet (-500 Kcal/day) plus liraglutide (n = 23) or placebo (n = 27) for 14 weeks. The groups were similar in age (58 ± 7 vs. 58 ± 8 years) and body mass index (31.9 ± 2.8 vs. 31.9 ± 3.5 kg/m(2)). A comprehensive lipid/lipoprotein profile was obtained before and after intervention using vertical auto profile (VAP). Weight loss was greater in the liraglutide group than in the placebo group (6.9 vs. 3.3 kg, p < 0.001), as was the fall in fasting plasma glucose concentration (9.9 mg/dL vs. 0.3 mg/dL, p < 0.001). VAP analysis revealed multiple improvements in lipid/lipoprotein metabolism in liraglutide-treated compared with placebo-treated volunteers, including decreases in concentrations of total cholesterol, low-density lipoprotein cholesterol and several of its subclasses, triglyceride, and non-high-density cholesterol. The liraglutide-treated group also had a significant shift away from small, dense low-density lipoprotein-particles, as well as decreases in apolipoprotein B concentration and ratio of apolipoprotein B/apolipoprotein A-1. There were no significant changes in the lipoprotein profile in the placebo-treated group. CONCLUSION: Treatment with liraglutide plus modest calorie restriction led to enhanced weight loss, a decrease in fasting plasma glucose concentration, and improvement in multiple aspects of lipid/lipoprotein metabolism associated with increased cardiovascular disease (CVD) risk. The significant clinical benefit associated with liraglutide-assisted weight loss in a group at high risk for CVD - obese/overweight individuals with prediabetes - as seen in our pilot study, suggests that this approach deserves further study.

Is measurement of non-HDL cholesterol an effective way to identify the metabolic syndrome?

BACKGROUND AND AIMS: The metabolic syndrome (MetS) has been shown to predict coronary heart disease (CHD). Non-high-density lipoprotein cholesterol (non-HDL-C) is also known to predict CHD, and recent evidence indicated non-HDL-C was able to predict MetS in adolescents. The study aim was to determine whether non-HDL-C serves as a useful metabolic marker for MetS in adults. METHODS AND RESULTS: Fasting non-HDL-C measurements were obtained in 366 non-diabetic adults not on lipid-lowering therapy. In addition to traditional non-HDL-C cut-points based on Adult Treatment Panel III guidelines, receiver-operating characteristic curve analysis was used to identify an optimal cut-point for predicting MetS. A secondary goal was to assess the relationship between non-HDL-C and insulin resistance, defined as the upper tertile of steady-state plasma glucose concentrations measured during the insulin suppression test. Prevalence of MetS was 40% among participants. Those with MetS had higher mean non-HDL-C (4.17 ± 1.0 vs 3.70 ± 0.85 mmol/L, p < 0.001), and the upper vs lower tertile of non-HDL-C concentrations was associated with 1.8-fold increased odds of MetS (p < 0.05). Traditional non-HDL-C cut-points ≥ 4.14 and ≥ 4.92 mmol/L demonstrated respective sensitivities 46% and 24% (specificities 72% and 89%) for identifying MetS. The optimal non-HDL-C cut-point ≥ 4.45 mmol/L had sensitivity 39% (specificity 82%). Comparable results were observed when non-HDL-C was used to identify insulin resistance. CONCLUSION: While MetS was associated with increased non-HDL-C, an effective non-HDL-C threshold to predict MetS in adults was not identified. Dyslipidemic nuances may explain why non-HDL-C may be less useful as a metabolic marker for MetS and/or insulin resistance than for CHD.

Identifying cardiovascular disease risk and outcome: use of the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio versus metabolic syndrome criteria.

BACKGROUND: Metabolic syndrome (MetS) has been shown to predict both risk and CVD events. We have identified sex-specific values for the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio associated with an unfavourable cardio-metabolic risk profile, but it is not known whether it also predicts CVD outcome. METHODS: To quantify risk for CVD outcomes associated with a high TG/HDL-C ratio and to compare this risk with that predicted using MetS, a population longitudinal prospective observational study was performed in Rauch City, Buenos Aires, Argentina. In 2003 surveys were performed on a population random sample of 926 inhabitants. In 2012, 527 women and 269 men were surveyed again in search of new CVD events. The first CVD event was the primary endpoint. Relative risks for CVD events between individuals above and below the TG/HDL-C cut-points, and with or without MetS, were estimated using Cox proportional hazard. MAIN OUTCOME: The first CVD event was the primary endpoint. Relative risks for CVD events between individuals above and below the TG/HDL-C cut-points, and with or without MetS, were estimated using Cox proportional hazard. RESULTS: The number of subjects deemed at 'high' CVD risk on the basis of an elevated TG/HDL-C ratio (30%) or having the MetS (35%) was relatively comparable. The unadjusted hazard risk was significantly increased when comparing 'high' versus 'low' risk groups no matter which criteria was used, although it was somewhat higher in those with the MetS (HR = 3.17, 95% CI:1.79-5.60 vs. 2.16, 95% CI:1.24-3.75). However, this difference essentially disappeared when adjusted for sex and age (HR = 2.09, 95% CI:1.18-3.72 vs. 2.01, 95% CI:1.14-3.50 for MetS and TG/HDL-C respectively). CONCLUSIONS: An elevated TG/HDL-C ratio appears to be just as effective as the MetS diagnosis in predicting the development of CVD.

Adiposity indices in the prediction of metabolic abnormalities associated with cardiovascular disease in non-diabetic adults.

BACKGROUND AND AIMS: The prevalence of insulin resistance and cardiovascular disease (CVD) increases with degree of obesity. Whether measurements of generalized and abdominal obesity differ in the ability to predict changes associated with increased CVD risk is widely debated. We compared the prevalence of metabolic abnormalities in 275 women and 204 men stratified by categories of body mass index (BMI) and waist circumference (WC), and assessed the ability of these adiposity indices in combination with metabolic risk variables to predict insulin resistance. METHODS AND RESULTS: Healthy, non-diabetic volunteers underwent measurements of BMI, WC, blood pressure, fasting plasma glucose (FPG), lipoprotein concentrations, and direct quantification of insulin-mediated glucose uptake. Insulin resistance was defined as the top tertile of steady-state plasma glucose (SSPG) concentrations. BMI and WC were highly correlated (P < 0.001) in both women and men. Abnormal SSPG and triglyceride concentrations were associated with increasing adiposity by either index in both genders. Among women, abnormal FPG and high density lipoprotein cholesterol (HDL-C) concentrations were associated with increasing BMI and WC. In men, abnormal HDL-C was associated with increasing BMI only. Elevated systolic blood pressure (SBP) was associated with increasing BMI in both genders. The odds of insulin resistance were greatest in women with elevated FPG and triglycerides (4.5-fold). In men, the best predictors were BMI and SBP, and WC and HDL-C (3-fold). CONCLUSION: BMI is at least comparable to WC in stratifying individuals for prevalence of metabolic abnormalities associated with increased CVD risk and predicting insulin resistance.

The metabolic syndrome: time to get off the merry-go-round?

The diagnostic category of the metabolic syndrome (MetS) has received considerable attention over the last decade, and prestigious organizations continue to strive for its incorporation into medical practice. This review has three goals: (i) summarize the history of the several attempts to define a diagnostic category designated as the MetS; (ii) question the aetiological role of abdominal obesity in the development of the other components of the MetS; and (iii) evaluate a diagnosis of the MetS as an effective way to identify apparently healthy individuals at increased risk to develop cardiovascular disease (CVD) or type 2 diabetes (2DM). The most important conclusion is that the MetS seems to be less effective in this population than the Framingham Risk Score in predicting CVD, and no better, if not worse, than fasting plasma glucose concentrations in predicting 2DM.

Pioglitazone increases the proportion of small cells in human abdominal subcutaneous adipose tissue.

Rodent and in vitro studies suggest that thiazolidinediones promote adipogenesis but there are few studies in humans to corroborate these findings. The purpose of this study was to determine whether pioglitazone stimulates adipogenesis in vivo and whether this process relates to improved insulin sensitivity. To test this hypothesis, 12 overweight/obese nondiabetic, insulin-resistant individuals underwent biopsy of abdominal subcutaneous adipose tissue at baseline and after 12 weeks of pioglitazone treatment. Cell size distribution was determined via the Multisizer technique. Insulin sensitivity was quantified at baseline and postpioglitazone by the modified insulin suppression test. Regional fat depots were quantified by computed tomography (CT). Insulin resistance (steady-state plasma insulin and glucose (SSPG)) decreased following pioglitazone (P < 0.001). There was an increase in the ratio of small-to-large cells (1.16 +/- 0.44 vs. 1.52 +/- 0.66, P = 0.03), as well as a 25% increase in the absolute number of small cells (P = 0.03). The distribution of large cell diameters widened (P = 0.009), but diameter did not increase in the case of small cells. The increase in proportion of small cells was associated with the degree to which insulin resistance improved (r = -0.72, P = 0.012). Visceral abdominal fat decreased (P = 0.04), and subcutaneous abdominal (P = 0.03) and femoral fat (P = 0.004) increased significantly. Changes in fat volume were not associated with SSPG change. These findings demonstrate a clear effect of pioglitazone on human subcutaneous adipose cells, suggestive of adipogenesis in abdominal subcutaneous adipose tissue, as well as redistribution of fat from visceral to subcutaneous depots, highlighting a potential mechanism of action for thiazolidinediones. These findings support the hypothesis that defects in subcutaneous fat storage may underlie obesity-associated insulin resistance.

The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation.

This article presents the conclusions of a WHO Expert Consultation that evaluated the utility of the 'metabolic syndrome' concept in relation to four key areas: pathophysiology, epidemiology, clinical work and public health. The metabolic syndrome is a concept that focuses attention on complex multifactorial health problems. While it may be considered useful as an educational concept, it has limited practical utility as a diagnostic or management tool. Further efforts to redefine it are inappropriate in the light of current knowledge and understanding, and there is limited utility in epidemiological studies in which different definitions of the metabolic syndrome are compared. Metabolic syndrome is a pre-morbid condition rather than a clinical diagnosis, and should thus exclude individuals with established diabetes or known cardiovascular disease (CVD). Future research should focus on: (1) further elucidation of common metabolic pathways underlying the development of diabetes and CVD, including those clustering within the metabolic syndrome; (2) early-life determinants of metabolic risk; (3) developing and evaluating context-specific strategies for identifying and reducing CVD and diabetes risk, based on available resources; and (4) developing and evaluating population-based prevention strategies.