A systematic review of non-genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis

Type 1 diabetes (T1D) results from autoimmune destruction of the pancreatic βcells. Although all T1D patients require daily administration of exogenous insulin, their insulin requirement to achieve good glycaemic control may vary significantly. Glycated haemoglobin (HbA1c) level represents a stable indicator of glycaemic control and is a reliable predictor of long-term complications of T1D. The purpose of this article is to systematically review the role of non-genetic predictors and genetic factors of HbA1c level in T1D patients after the first year of T1D, to exclude the honeymoon period. A total of 1974 articles published since January 2011 were identified and 78 were finally included in the analysis of non-genetic predictors. For genetic factors, a total of 277 articles were identified and 14 were included. The most significantly associated factors with HbA1c level are demographic (age, ethnicity, and socioeconomic status), personal (family characteristics, parental care, psychological traits...) and features related to T1D (duration of T1D, adherence to treatment …). Only a few studies have searched for genetic factors influencing HbA1c level, most of which focused on candidate genes using classical genetic statistical methods, with generally limited power and incomplete adjustment for confounding factors and multiple testing. Our review shows the complexity of explaining HbA1c level variations, which involves numerous correlated predictors. Overall, our review underlines the lack of studies investigating jointly genetic and non-genetic factors and their interactions to better understand factors influencing glycaemic control for T1D patients.

Comparison of the impact of human vs analogue insulins on glycosylated haemoglobin in a population with diabetes mellitus

AIMS

To compare the effect on metabolic control of treatment with conventional and analogue insulins for patients with diabetes mellitus.

METHODS

Retrospective cohort study held in cities of Colombia (Pereira and Manizales). People insured by the paid healthcare system, who were diagnosed with diabetes mellitus type 1 and 2, and treated with conventional and analogue insulin for at least 6 months prior to the start of the study were sampled and followed up for 18 months. Data were collected from clinical records for each patient. Treatment groups were compared according to the type of insulin received.

RESULTS

A total of 313 patients were included; overall, 56.9% were women and the mean age was 57.3 years. No statistically significant difference was found in glycosylated haemoglobin reduction at 3, 6 and 18 months when comparing patients receiving glargine vs NPH insulin (P=.403) and NPH plus zinc crystalline insulin vs glargine plus glulisine (P=.514). The percentage of patients with metabolic control increased from 27.8% to 34.2% during follow-up with all types of insulin.

CONCLUSIONS

Insulin analogues were not superior to human insulin for glycaemic control. A significant proportion of patients did not attain the treatment goals; therefore, it is necessary to implement measures to improve the monitoring and control of diabetes mellitus.

Use of high-normal levels of haemoglobin A(1C) and fasting plasma glucose for diabetes screening and for prediction: a meta-analysis

BACKGROUND

Using high-normal levels of haemoglobin A1C (Abnormal-A1C ) or fasting plasma glucose (FPG) (Abnormal-FPG) for diabetes screening are expected to improve the ability to detect persons with or at high risk of diabetes. We assessed the diagnostic and predictive capacity for diabetes of Abnormal-A1C and Abnormal-FPG. We compared these to the combined use of the two measures to the single use of either measurement.

METHODS

We analysed 31 eligible cross-sectional or cohort studies that assessed diagnostic or predictive ability, respectively, by using lower A1C and FPG cutoff values than recommended by current diabetes criteria. Positive and negative likelihood ratios (LR+ and LR-) were calculated to assess the ability to confirm or exclude diabetes, respectively, on the basis of a bivariate random-effects model.

RESULTS

With both Abnormal-A1C and Abnormal-FPG, the pooled LR+ was above 4 for diagnosing diabetes and above 3 for predicting diabetes. However, the pooled LR- for predicting diabetes was higher with Abnormal-A1C (0.48) and Abnormal-FPG (0.49) in comparison with that for diagnosing diabetes (0.27, Abnormal-A1C ; 0.28, Abnormal-FPG). In eight studies that assessed the predictive ability of the combination of A1C and FPG, using either Abnormal-A1C or Abnormal-FPG could lower LR- to 0.17 from 0.43 for only Abnormal-A1C and from 0.38 for only Abnormal-FPG. Accordingly, LR+ was also lowered to 2.37 from 3.36 for only Abnormal-A1C and from 3.84 for only-Abnormal-FPG.

CONCLUSION

The use of the two blood glucose tests had insufficient capacity to identify subjects at high risk for diabetes but had considerable capacity to identify undiagnosed diabetes.