McAuley Index Research Articles

Significant correlation between BMI/BW with insulin resistance by McAuley, HOMA and QUICKI indices after 3 months of pioglitazone in diabetic population

Twenty-four patients of type 2 diabetes with fasting blood glucose (FBG) > 7 mmol/l (126 mg/dl) were randomly selected on one (if the patient is symptomatic) or two occasions (if the patient is asymptomatic). Patients were treated with 15 mg of PIO daily and investigated for BW, BMI, FBG, fasting insulin (FI) and triglycerides (TG). IR was calculated by McAuley (McA), homeostasis model assessment (HOMA) and QUICKI indices at baseline and repeated after 3 months.

Fasting‐based Estimates of Insulin Sensitivity in Overweight and Obesity: A Critical Appraisal

To identify simple methods to estimate the degree of insulin resistance. The performance of a wide range of fasting-based index estimates of insulin sensitivity was compared by receiver operating characteristic analysis (area under curves and their 95% confidence intervals) against the M value from euglycemic insulin clamp studies collected in the San Antonio (non-Hispanic whites and Hispanic residents of San Antonio, TX) and European Group for the Study of Insulin Resistance (non-diabetic white Europeans) databases (n = 638). Insulin resistance differed substantially between lean (BMI < 25 kg/m2), overweight or obese (BMI > or = 25 kg/m2), and type 2 diabetic individuals. Estimates of insulin resistance were, therefore, assessed in each group separately. In the overweight and obese subgroup (n = 302), the receiver operating characteristic performance of fasting-based indices varied from 0.72 (0.62 to 0.82), in the case of the insulin/glucose ratio, to 0.80 (0.72 to 0.88) in the case of Belfiore free fatty acids. One superior method could not be identified; the confidence intervals overlapped, and no statistically significant differences emerged. All indices performed better when using the whole study population, with fasting plasma insulin, homeostatic model assessment, insulin/glucose ratio, quantitative insulin sensitivity check index, glucose/insulin ratio, Belfiore glycemia, revised quantitative insulin sensitivity check index, McAuley index, and Belfiore free fatty acids showing area under curves of 0.83, 0.90, 0.66, 0.90, 0.66, 0.90, 0.85, 0.83, and 0.86, respectively, because of the inclusion of very insulin sensitive (lean) and very insulin resistant cases (diabetic subjects). In conclusion, a superior fasting-based index estimate to distinguish between the presence and absence of insulin resistance in overweight and obesity could not be identified despite the use of the large datasets.

Metabolic syndrome throughout the menopausal transition: influence of age and menopausal status

Objective To assess the relationship between the main components of both the metabolic syndrome and insulin resistance and menopausal status in the menopausal transition.Methods A total of 124 healthy women were divided into four groups according to their menstrual status: the first group consisted of 35 women in menopausal transition with menstrual bleeding (MTM) and with cycles between 35 and 80 days; the second group was composed of 29 women in menopausal transition with 3–6 months of amenorrhea (MTA). The third group consisted of 31 postmenopausal women (PostM) and the fourth group of 29 premenopausal women (PreM) with regular cycles. The metabolic syndrome was evaluated following the ATP III criteria. Evaluation of insulin resistance was made through the HOMA, QUICKI and McAuley indices and the triglycerides/high density lipoprotein (HDL) cholesterol ratio.Results The triglycerides/HDL cholesterol ratio increased in MTM, MTA and PostM women in comparison with PreM women. A slight decrease in the QUIKI index (p = 0.06) and a decrease in the McAuley index (p < 0.001) were observed in MTM, MTA and PostM women in comparison to PreM women. The relative frequencies of metabolic syndrome in the four groups were: PreM, 0%; MTM, 20%; MTA, 21%; and PostM, 22% (p = 0.0001). The most frequent markers of the metabolic syndrome were increased waist circumference, low HDL cholesterol levels and hypertension. Linear regression between menopausal status and metabolic syndrome was lost when age was added to the model.Conclusions The frequency of metabolic syndrome increased from the time of the menopausal transition to the postmenopause. Abdominal obesity was the most frequent feature observed. Nevertheless, aging erased the effect of the menopause on the metabolic syndrome. In order to prevent cardiovascular disease, the metabolic syndrome must be evaluated from the time of the menopausal transition.

High Plasma Leptin Is Not Associated with Higher Bone Mineral Density in Insulin-Resistant Premenopausal Obese Women

Obesity's protective effect on bone density may be mediated through increased muscle mass, fat mass, increased estrogen, and possibly insulin and leptin levels. To determine the impact of leptin and insulin on bone metabolism, we studied 48 obese normally cycling premenopausal women (age, 31 +/- 10 yr; body mass index, 35.7 +/- 5 kg/m2): 28 insulin resistant (IR) and 20 insulin sensitive (IS) by McAuley index. Anthropometric, body composition, and bone mineral density (BMD) measurements were made, and serum leptin, insulin, free testosterone, IGF-I, bone remodeling markers, and calciotropic hormones were measured. Anthropometric, lifestyle, and biochemical markers were similar in the two groups. Despite higher circulating insulin and leptin levels, IR subjects had similar mean values of serum osteocalcin but higher C-telopeptide (P = 0.052). They had similar BMD at all skeletal sites compared with IS subjects. In the IR group, fat mass but not lean mass, serum leptin, insulin, testosterone, and IGF-I levels correlated positively with hip and/or total-body bone density with R varying between 0.38 and 0.65; no correlations were observed at the spine. Conversely, in the IS group, lean mass, but not fat mass, and only IGF-I correlated with hip BMD/total-body bone mineral content. In conclusion, there is a dichotomy in the impact of body composition parameters and insulin and leptin levels on bone parameters in obese individuals. The interaction between the fat-related endocrine system and bone seems to be complex and may be modulated by local resistance to the putative protective effect of insulin and leptin on bone.

Elevated intraocular pressure is associated with insulin resistance and metabolic syndrome

Elevated intraocular pressure (IOP), a well-known risk factor for glaucoma, has recently been shown to be associated with some metabolic complications and obesity. We investigated the link between IOP and metabolic disturbances, focusing especially on metabolic syndrome and insulin resistance. Eye examinations, including IOP measurement, were conducted on 943 subjects (533 men and 410 women). Body mass index (BMI), percent body fat, waist circumference, systolic and diastolic pressure, fasting insulin, glucose, lipids, and other metabolic parameters were measured. The homeostasis model assessment (HOMA) score and McAuley index were calculated to assess whole-body insulin resistance. Both of these insulin resistance indices showed positive associations with IOP (p < 0.05), even after statistical adjustment for other risk factors. IOP was higher in participants with metabolic syndrome, as compared to those who did not have metabolic syndrome. The mean IOP tended to increase linearly with the presence of increasing numbers of components for metabolic syndrome. These results suggest that insulin resistance might contribute to an explanation that would account for many previous findings concerning the association between IOP and obesity, hypertension, and diabetes.

Diagnosing Insulin Resistance by Simple Quantitative Methods in Subjects With Normal Glucose Metabolism

Ascaso et al. (1) evaluated insulin sensitivity in 65 healthy subjects using minimal model analysis of an insulin-modified frequently sampled intravenous glucose tolerance test (FSIVGTT) to estimate the insulin sensitivity parameter derived from minimal model (SIMM) (2), as well as simple surrogates including fasting insulin, homeostasis model assessment (3), QUICKI (4), and the McAuley index (5). A statistically significant correlation was observed between SIMM and all of the other surrogate indexes examined. However, using SIMM as the reference method, the authors claim that the McAuley index has the best correlation with SI …