People with the same glucose tolerance may demonstrate variable plasma 1,5AG concentrations depending on their renal threshold for glucose. This inherent characteristic is likely to limit the usefulness of the test when monitoring or screening for diabetes.

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Women with polycystic ovary syndrome (PCOS) were found to have a higher biological variability in insulin resistance (IR) compared to controls, but it is unknown whether this variability in IR differs between PCOS who are anovulatory compared to those who have an ovulatory cycle. The primary aim of this study was to compare and contrast the variability of IR in women with ovulatory and anovulatory PCOS, in comparison to normal subjects. 53 Caucasian women with PCOS and 22 normal ovulating women were recruited. Fasting blood was collected each day on 10 consecutive occasions at 3-4 day intervals for analysis of insulin, glucose, progesterone, and testosterone. Analysis of progesterone levels showed 22 of 53 women with PCOS to have had an ovulatory cycle. Insulin resistance was calculated by HOMA method. Women with anovulatory PCOS had higher mean and variability of IR compared to those having an ovulatory cycle, and both were significantly higher than controls (mean ± SEM; HOMA-IR 4.14 ± 0.14 vs. 3.65 ± 0.15 vs. 2.21 ± 0.16, respectively) after adjustment or BMI. The mean BMI for individual PCOS patients correlated with mean HOMA-IR (p=0.009). Insulin resistance in women with anovulatory PCOS is both higher and more variable than in ovulatory PCOS. Since anovulatory PCOS therefore mimics the IR features of type 2 diabetes more closely, anovulation may be particularly associated with a higher cardiovascular risk compared to PCOS patients who ovulate.

The Diabetes Control and Complications Trial (DCCT) has provided objective evidence for desirable glycaemic control in Type 1 patients and defines the benefits of good glycaemic control in terms of haemoglobin A1c (HbA1c) values. However, HbA1c assays vary, leading to suggestions that glycaemic control be classified according to numbers of standard deviations (SD) from a local non-diabetic population mean. We have classified the glycaemic control of 339 UK Type 1 diabetic patients (182 male, 157 female, median age 36 (range 15-74) years) using the DCCT to set HbA1c targets and compared this with the SD method. Using age matched controls (mean HbA1c 4.02%, SD 0.28%, n=106), SD guidelines classified 1% of patients into good (HbA1c <3SD from reference mean), 4% into borderline (3-5SD) and 95% into poor (>5SD) glycaemic control. When calibrating the same instrument to the DCCT analyser (r=0.996), 37% of patients had HbA1c results lower than the 7% median value found in the intensively treated DCCT group, while only 12% of patients had values greater than the 9% conventionally treated median HbA1c. DCCT subjects with HbA1c values of less than 8% belonged predominantly to the intensively treated group. In this study, 71% of patients fell into this category. Thus, guidelines based on numbers of SD away from a non-diabetic mean may overestimate the glycaemic control required to reduce microvascular complications in Type 1 patients. Standardizing to DCCT targets is more appropriate.

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