Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders, affecting 5%-10% of women of reproductive age. The importance of this syndrome lies in the magnitude of associated comorbidities: infertility, metabolic dysfunction, cardiovascular disease (CVD), plus psychological and oncological complications. Insulin resistance (IR) is a prominent feature of PCOS with a prevalence of 35%-80%. Without adequate management, IR with compensatory hyperinsulinemia contributes directly to reproductive dysfunction in women with PCOS. Furthermore, epidemiological data shows compelling evidence that PCOS is associated with an increased risk of impaired glucose tolerance, gestational diabetes mellitus and type 2 diabetes. In addition, metabolic dysfunction leads to a risk for CVD that increases with aging in women with PCOS. Indeed, the severity of IR in women with PCOS is associated with the amount of abdominal obesity, even in lean women with PCOS. Given these drastic implications, it is important to diagnose and treat insulin resistance as early as possible. Many markers have been proposed. However, quantitative assessment of IR in clinical practice remains a major challenge. The gold standard method for assessing insulin sensitivity is the hyperinsulinemic euglycemic glucose clamp. However, it is not used routinely because of the complexity of its procedure. Consequently, there has been an urgent need for surrogate markers of IR that are more applicable in large population-based epidemiological investigations. Despite this, many of them are either difficult to apply in routine clinical practice or useless for women with PCOS. Considering this difficulty, there is still a need for an accurate marker for easy, early detection and assessment of IR in women with PCOS. This review highlights markers of IR already used in women with PCOS, including new markers recently reported in literature, and it establishes a new classification for these markers.

Indian Asians living in the UK have a 50% higher CHD mortality rate compared with the indigenous Caucasian population, which cannot be attributed to traditional risk factors. Instead, features of the metabolic syndrome, including raised plasma triacylglycerol, reduced HDL-cholesterol (HDL-C) and an increased proportion of small dense LDL particles, together with insulin resistance and central obesity, are prevalent among this population. The present review examines evidence to support the hypothesis that an imbalance in dietary PUFA intake, specifically a higher intake of n-6 PUFA in combination with the lower intake of the long-chain (LC) n-3 PUFA, plays an important role in the prevalence of the metabolic syndrome observed in Indian Asians. Data are presented to illustrate the impact of manipulation of the background n-6 PUFA intake (moderate or high n-6 PUFA) and the subsequent response to supplementation with LC n-3 PUFA on blood lipids and insulin action in a group of Indian Asian volunteers. The results demonstrate that supplementation with LC n-3 PUFA had no impact on insulin action in those subjects consuming either the moderate- or high-n-6 PUFA diet. In the postprandial phase reductions in plasma triacylglycerol concentrations were greater in those consuming the high-n-6 PUFA background diet subsequent to fish oil supplementation. The present study concludes that, contrary to the central hypothesis, the prevalence of metabolic abnormalities in Indian Asians compared with Caucasians may not be attributable to differences in intakes of n-6 and n-3 PUFA.

Indian Asians in Western countries have a higher rate of coronary artery disease than do the indigenous white populations, and this higher rate may be influenced by a dietary imbalance of n-6 and n-3 polyunsaturated fatty acids (PUFAs).

The objective of the study was to test the hypothesis that a high background dietary intake of n-6 PUFA attenuates the effects of fish-oil supplementation on insulin sensitivity and associated blood lipids of the metabolic syndrome.

Twenty-nine Indian Asian men were recruited to participate in a 12-wk dietary intervention trial. Volunteers were randomly assigned to receive either a moderate or a high n-6 PUFA diet featuring modified oils and spreads over a 6-wk period. After this 6-wk period, both groups were supplemented with 4.0 g fish oil/d (2.5 g eicosapentaenoic acid + docosahexaenoic acid) for an additional 6 wk in combination with the dietary treatment. Volunteers participated in a postprandial study and an insulin sensitivity test after the 6-wk dietary intervention and again after the fish-oil supplementation period.

There was no significant time x treatment interaction for blood lipids or insulin action after dietary intervention with the moderate or high n-6 PUFA diets in combination with fish oil. After the 6-wk period of fish oil supplementation, fasting and postprandial plasma triacylglycerol concentrations decreased significantly.

The background dietary n-6 PUFA concentration did not modulate the effect of fish-oil supplementation on blood lipids or measures of insulin sensitivity in this ethnic group.

To compare insulin sensitivity (Si) from a frequently sampled intravenous glucose tolerance test (FSIGT) and subsequent minimal model analyses with surrogate measures of insulin sensitivity and resistance and to compare features of the metabolic syndrome between Caucasians and Indian Asians living in the UK.

In all, 27 healthy male volunteers (14 UK Caucasians and 13 UK Indian Asians), with a mean age of 51.2+/-1.5 y, BMI of 25.8+/-0.6 kg/m(2) and Si of 2.85+/-0.37.

Si was determined from an FSIGT with subsequent minimal model analysis. The concentrations of insulin, glucose and nonesterified fatty acids (NEFA) were analysed in fasting plasma and used to calculate surrogate measure of insulin sensitivity (quantitative insulin sensitivity check index (QUICKI), revised QUICKI) and resistance (homeostasis for insulin resistance (HOMA IR), fasting insulin resistance index (FIRI), Bennetts index, fasting insulin, insulin-to-glucose ratio). Plasma concentrations of triacylglycerol (TAG), total cholesterol, high density cholesterol, (HDL-C) and low density cholesterol, (LDL-C) were also measured in the fasted state. Anthropometric measurements were conducted to determine body-fat distribution.

Correlation analysis identified the strongest relationship between Si and the revised QUICKI (r=0.67; P=0.000). Significant associations were also observed between Si and QUICKI (r=0.51; P=0.007), HOMA IR (r=-0.50; P=0.009), FIRI and fasting insulin. The Indian Asian group had lower HDL-C (P=0.001), a higher waist-hip ratio (P=0.01) and were significantly less insulin sensitive (Si) than the Caucasian group (P=0.02).

The revised QUICKI demonstrated a statistically strong relationship with the minimal model. However, it was unable to differentiate between insulin-sensitive and -resistant groups in this study. Future larger studies in population groups with varying degrees of insulin sensitivity are recommended to investigate the general applicability of the revised QUICKI surrogate technique.

Given that the "gold standard" method for evaluating insulin sensitivity in vivo (hyperinsulinemic euglycemic glucose clamp technique) cannot be routinely applied because of technical reasons, simple methods and indexes were developed and are currently available to assess insulin sensitivity in vivo. Quantitative insulin sensitivity check index (QUICKI) has recently been described and is able to accurately estimate insulin sensitivity from a fasting blood sample. We demonstrated that fasting insulin levels strongly inversely correlated with QUICKI in three different groups: 215 healthy nondiabetic nonobese subjects, 62 nondiabetic obese subjects, and 44 patients with glucose intolerance or type 2 diabetes mellitus. Fasting insulin measurement is a simple way of assessing insulin sensitivity in obese and nonobese humans, with or without glucose intolerance or type 2 diabetes mellitus.