Biological variation of hemoglobin A1c: consequences for diagnosing diabetes mellitus

Exploring the role of eRNA in regulating gene expression

eRNAs as the products of enhancers can regulate gene expression via various possible ways, but which regulation way is more reasonable is debatable in biology, and in particular, how eRNAs impact gene expression remains unclear. Here we introduce a mechanistic model of gene expression to address these issues. This model considers three possible regulation ways of eRNA: Type-I by which eRNA regulates transcriptional activity by facilitating the formation of enhancer-promoter (E-P) loop, Type-II by which eRNA directly promotes the mRNA production rate, and mixed regulation (i.e., the combination of Type-I and Type-II). We show that with the increase of the E-P loop length, mRNA distribution can transition from unimodality to bimodality or vice versa in all the three regulation cases. However, in contrast to the other two regulations, Type-II regulation can lead to the highest mean mRNA level and the lowest mRNA noise, independent of the E-P loop length. These results would not only reveal the essential mechanism of how eRNA regulates gene expression, but also imply a new mechanism for phenotypic switching, namely the E-P loop can induce phenotypic switching.

Determinants of diagnostic discordance for non-diabetic hyperglycaemia and Type 2 diabetes using paired glycated haemoglobin measurements in a large English primary care population: cross-sectional study

AIM

To investigate factors influencing diagnostic discordance for non-diabetic hyperglycaemia and Type 2 diabetes.

METHODS

Some 10 000 adults at increased risk of diabetes were screened with HbA_1c and fasting plasma glucose (FPG). The 2208 participants with initial HbA_1c ≥ 42 mmol/mol (≥ 6.0%) or FPG ≥ 6.1 mmol/l were retested after a median 40 days. We compared the first and second HbA_1c results, and consequent diagnoses of non-diabetic hyperglycaemia and Type 2 diabetes, and investigated predictors of discordant diagnoses.

RESULTS

Of 1463 participants with non-diabetic hyperglycaemia and 394 with Type 2 diabetes on first testing, 28.4% and 21.1% respectively had discordant diagnoses on repeated testing. Initial diagnosis of non-diabetic hyperglycaemia and/or impaired fasting glucose according to both HbA_1c and FPG criteria, or to FPG only, made reclassification as Type 2 diabetes more likely than initial classification according to HbA_1c alone. Initial diagnosis of Type 2 diabetes according to both HbA_1c and FPG criteria made reclassification much less likely than initial classification according to HbA_1c alone. Age, and anthropometric and biological measurements independently but inconsistently predicted discordant diagnoses and changes in HbA_1c .

CONCLUSIONS

Diagnosis of non-diabetic hyperglycaemia or Type 2 diabetes with a single measurement of HbA_1c in a screening programme for entry to diabetes prevention trials is unreliable. Diagnosis of non-diabetic hyperglycaemia and Type 2 diabetes should be confirmed by repeat testing. FPG results could help prioritise retesting. These findings do not apply to people classified as normal on a single test, who were not retested.

Discordance in glycemic categories and regression to normality at baseline in 10,000 people in a Type 2 diabetes prevention trial

The world diabetes population quadrupled between 1980 and 2014 to 422 million and the enormous impact of Type 2 diabetes is recognised by the recent creation of national Type 2 diabetes prevention programmes. There is uncertainty about how to correctly risk stratify people for entry into prevention programmes, how combinations of multiple 'at high risk' glycemic categories predict outcome, and how the large recently defined 'at risk' population based on an elevated glycosylated haemoglobin (HbA1c) should be managed. We identified all 141,973 people at highest risk of diabetes in our population, and screened 10,000 of these with paired fasting plasma glucose and HbA1c for randomisation into a very large Type 2 diabetes prevention trial. Baseline discordance rate between highest risk categories was 45.6%, and 21.3-37.0% of highest risk glycaemic categories regressed to normality between paired baseline measurements (median 40 days apart). Accurate risk stratification using both fasting plasma glucose and HbA1c data, the use of paired baseline data, and awareness of diagnostic imprecision at diagnostic thresholds would avoid substantial overestimation of the true risk of Type 2 diabetes and the potential benefits (or otherwise) of intervention, in high risk subjects entering prevention trials and programmes.

The Biological Variation Data Critical Appraisal Checklist: A Standard for Evaluating Studies on Biological Variation

BACKGROUND

Concern has been raised about the quality of available biological variation (BV) estimates and the effect of their application in clinical practice. A European Federation of Clinical Chemistry and Laboratory Medicine Task and Finish Group has addressed this issue. The aim of this report is to (a) describe the Biological Variation Data Critical Appraisal Checklist (BIVAC), which verifies whether publications have included all essential elements that may impact the veracity of associated BV estimates, (b) use the BIVAC to critically appraise existing BV publications on enzymes, lipids, kidney, and diabetes-related measurands, and (c) apply metaanalysis to deliver a global within-subject BV (CV_I) estimate for alanine aminotransferase (ALT).

METHODS

In the BIVAC, publications were rated as A, B, C, or D, indicating descending compliance for 14 BIVAC quality items, focusing on study design, methodology, and statistical handling. A D grade indicated that associated BV estimates should not be applied in clinical practice. Systematic searches were applied to identify BV studies for 28 different measurands.

RESULTS

In total, 128 publications were identified, providing 935 different BV estimates. Nine percent achieved D scores. Outlier analysis and variance homogeneity testing were scored as C in >60% of 847 cases. Metaanalysis delivered a CV_I estimate for ALT of 15.4%.

CONCLUSIONS

Application of BIVAC to BV publications identified deficiencies in required study detail and delivery, especially for statistical analysis. Those deficiencies impact the veracity of BV estimates. BV data from BIVAC-compliant studies can be combined to deliver robust global estimates for safe clinical application.

Performance of point-of-care HbA1c test devices: implications for use in clinical practice - a systematic review and meta-analysis

BACKGROUND

Point-of-care (POC) devices could be used to measure hemoglobin A1c (HbA1c) in the doctors' office, allowing immediate feedback of results to patients. Reports have raised concerns about the analytical performance of some of these devices. We carried out a systematic review and meta-analysis using a novel approach to compare the accuracy and precision of POC HbA1c devices.

METHODS

Medline, Embase and Web of Science databases were searched in June 2015 for published reports comparing POC HbA1c devices with laboratory methods. Two reviewers screened articles and extracted data on bias, precision and diagnostic accuracy. Mean bias and variability between the POC and laboratory test were combined in a meta-analysis. Study quality was assessed using the QUADAS2 tool.

RESULTS

Two researchers independently reviewed 1739 records for eligibility. Sixty-one studies were included in the meta-analysis of mean bias. Devices evaluated were A1cgear, A1cNow, Afinion, B-analyst, Clover, Cobas b101, DCA 2000/Vantage, HemoCue, Innovastar, Nycocard, Quo-Lab, Quo-Test and SDA1cCare. Nine devices had a negative mean bias which was significant for three devices. There was substantial variability in bias within devices. There was no difference in bias between clinical or laboratory operators in two devices.

CONCLUSIONS

This is the first meta-analysis to directly compare performance of POC HbA1c devices. Use of a device with a mean negative bias compared to a laboratory method may lead to higher levels of glycemia and a lower risk of hypoglycaemia. The implications of this on clinical decision-making and patient outcomes now need to be tested in a randomized trial.

Evaluation of a new hemoglobin A1c analyzer for point-of-care testing

BACKGROUND

There has been an increasing desire for the use of point-of-care testing (POCT) by both primary care clinicians and patients. This study aimed to evaluate the performance of a new POCT analyzer for hemoglobin A1c (HbA1c) testing.

METHODS

We assessed the accuracy, precision, and linearity of the POCT HbA1c analyzer (A1C EZ 2.0) with the Tosoh G8 Analyzer as comparative instrument, following the Clinical and Laboratory Standards Institute (CLSI) protocols. We evaluated sensitivity and specificity of the A1C EZ 2.0 in the clinical diagnosis of diabetes among 842 subjects from 79 communities in Beijing, China.

RESULTS

Using regression analysis, the slope of the A1C EZ 2.0 vs the Tosoh G8 Analyzer was 0.9938, with an intercept of 0.0964 and a concordance correlation coefficient of 0.978. For precision, the reproducibility of CV (CV_T ) were 3.7% and 2.7% at a lower (36 mmol/mol (5.4%)) and higher (107 mmol/mol (11.9%)) level of HbA1c respectively. The area under the receiver operating characteristic (ROC) curve for clinical diagnosis of diabetes was 0.911 with the HbA1c cut-off value of 44 mmol/mol (6.14%). At the HbA1c level of 48 mmol/mol (6.5%), the sensitivity and specificity were76.1% and 86.6%.

CONCLUSION

The A1C EZ 2.0 has a high accuracy and precision, with a wide range of linearity, compared to a comparative laboratory instrument. It met analytical quality specifications and could be suitable for the clinical management of diabetes mellitus.

Understanding the Use of Sigma Metrics in Hemoglobin A1c Analysis

This study uses three unique data sets to show the state of the art of hemoglobin A1c (HbA1c) analyzers in a range of settings and compares their performance against the international guidance set by the International Federation of Clinical Chemistry and Laboratory Medicine task force for HbA1c standardization. The data are used to show the effect of tightening those criteria, and the study serves as a guide to the practical implementation of the sigma-metrics approach in a range of clinical settings.

Whole Blood Donation Affects the Interpretation of Hemoglobin A1c

Introduction

Several factors, including changed dynamics of erythrocyte formation and degradation, can influence the degree of hemoglobin A_1c (HbA_1c) formation thereby affecting its use in monitoring diabetes. This study determines the influence of whole blood donation on HbA_1c in both non-diabetic blood donors and blood donors with type 2 diabetes.

Methods

In this observational study, 23 non-diabetic blood donors and 21 blood donors with type 2 diabetes donated 475 mL whole blood and were followed prospectively for nine weeks. Each week blood samples were collected and analyzed for changes in HbA_1c using three secondary reference measurement procedures.

Results

Twelve non-diabetic blood donors (52.2%) and 10 (58.8%) blood donors with type 2 diabetes had a significant reduction in HbA_1c following blood donation (reduction >-4.28%, P < 0.05). All non-diabetic blood donors with a normal ferritin concentration predonation had a significant reduction in HbA_1c. In the non-diabetic group the maximum reduction was -11.9%, in the type 2 diabetes group -12.0%. When eligible to donate again, 52.2% of the non-diabetic blood donors and 41.2% of the blood donors with type 2 diabetes had HbA_1c concentrations significantly lower compared to their predonation concentration (reduction >-4.28%, P < 0.05).

Conclusion

Patients with type 2 diabetes contributing to whole blood donation programs can be at risk of falsely lowered HbA_1c. This could lead to a wrong interpretation of their glycemic control by their general practitioner or internist.

Methods, units and quality requirements for the analysis of haemoglobin A1c in diabetes mellitus

The formation of glycohemoglobin, especially the hemoglobin A_1c (HbA_1c) fraction, occurs when glucose becomes coupled with the amino acid valine in the β-chain of Hb; this reaction is dependent on the plasma concentration of glucose. Since the early 1970s it has been known that diabetics display higher values OF HbA_1C because they have elevated blood glucose concentrations. Thus HbA_1c has acquired a very important role in the treatment and diagnosis of diabetes mellitus. After the introduction of the first quantitative measurement OF HbA_1C, numerous methods for glycohemoglobin have been introduced with different assay principles: From a simple mini-column technique to the very accurate automated high-pressure chromatography and lastly to many automated immunochemical or enzymatic assays. In early days, the results of the quality control reports for HbA_1c varied extensively between laboratories, therefore in United States and Canada working groups (WG) of the Diabetes Controls and Complications Trial (DCCT) were set up to standardize the HbA_1c assays against the DCCT/National Glycohemoglobin Standardization Program reference method based on liquid chromatography. In the 1990s, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) appointed a new WG to plan a reference preparation and method for the HBA_1c measurement. When the reference procedures were established, in 2004 IFCC recommended that all manufacturers for equipment used in HbA_1c assays should calibrate their methods to their proposals. This led to an improvement in the coefficient of variation (CV%) associated with the assay. In this review, we describe the glycation of Hb, methods, standardization of the HbA_1c assays, analytical problems, problems with the units in which HbA_1c values are expressed, reference values, quality control aspects, target requirements for HbA_1c, and the relationship of the plasma glucose values to HbA_1c concentrations. We also note that the acceptance of the mmol/mol system for HbA_1c as recommended by IFCC, i.e., the new unit and reference ranges, are becoming only slowly accepted outside of Europe where it seems that expressing HbA_1c values either only in per cent units or with parallel reporting of percent and mmol/mol will continue. We believe that these issues should be resolved in the future and that it would avoid confusion if mmol/mol unit for HbA_1c were to gain worldwide acceptance.

HbA1c and Risks of All-Cause and Cause-Specific Death in Subjects without Known Diabetes: A Dose-Response Meta-Analysis of Prospective Cohort Studies

Whether HbA_1c levels are associated with mortality in subjects without known diabetes remains controversial. Moreover, the shape of the dose–response relationship on this topic is unclear. Therefore, a dose–response meta-analysis was conducted. PubMed and EMBASE were searched. Summary hazard ratios (HRs) were calculated using a random-effects model. Twelve studies were included. The summary HR per 1% increase in HbA_1c level was 1.03 [95% confidence interval (CI) = 1.01–1.04] for all-cause mortality, 1.05 [95% CI = 1.02–1.07) for cardiovascular disease (CVD) mortality, and 1.02 (95% CI = 0.99–1.07) for cancer mortality. After excluding subjects with undiagnosed diabetes, the aforementioned associations remained significant for CVD mortality only. After further excluding subjects with prediabetes, all aforementioned associations presented non-significance. Evidence of a non-linear association between HbA_1c and mortality from all causes, CVD and cancer was found (all P_{non-linearity} < 0.05). The dose–response curves were relatively flat for HbA_1c less than around 5.7%, and rose steeply thereafter. In conclusion, higher HbA_1c level is associated with increased mortality from all causes and CVD among subjects without known diabetes. However, this association is driven by those with undiagnosed diabetes or prediabetes. The results regarding cancer mortality should be treated with caution due to limited studies.

Reliability of biological variation data available in an online database: need for improvement

BACKGROUND

Biological variation (BV) data enable assessment of the significance of changes in serial measurements observed within a subject and are used to set analytical quality specifications. This data is available in a database held in Westgard website (http://www.westgard.com/biodatabase1.htm). Some limitations of this data, however, have been identified in recent published reviews. The aim of this paper is to show the reliability of the published BV data and to identify ongoing works to address some of its limitations.

METHODS

The BV data currently hosted on the Westgard website was examined. Distribution of measurands stratified by the number of cited references upon which the database entry is based and the distribution of papers stratified by publication year, are shown. Moreover, BV data available in literature for glycated hemoglobin, C-reactive protein, glycated albumin, alanine aminotransferase, aspartate aminotransferase and γ-glutamyl transferase are evaluated.

RESULTS

The results obtained show that most BV data come just from a few papers or only one paper and that a lot of publications are dated, therefore this data is too obsolete to be used. Furthermore critical review of the BV database highlights a number of factors that might impact on the reliability of the BV data entries and translation into current practice.

CONCLUSIONS

A number of issues clearly undermine the value of the current database. These issues are being considered by the European Federation of Clinical Chemistry and Laboratory Medicine, biological variation working group, in collaboration with a Spanish group responsible for the database updating.

Investigation of 2 models to set and evaluate quality targets for hb a1c: biological variation and sigma-metrics

BACKGROUND

A major objective of the IFCC Task Force on Implementation of HbA1c Standardization is to develop a model to define quality targets for glycated hemoglobin (Hb A1c).

METHODS

Two generic models, biological variation and sigma-metrics, are investigated. We selected variables in the models for Hb A1c and used data of external quality assurance/proficiency testing programs to evaluate the suitability of the models to set and evaluate quality targets within and between laboratories.

RESULTS

In the biological variation model, 48% of individual laboratories and none of the 26 instrument groups met the minimum performance criterion. In the sigma-metrics model, with a total allowable error (TAE) set at 5 mmol/mol (0.46% NGSP), 77% of the individual laboratories and 12 of 26 instrument groups met the 2σ criterion.

CONCLUSIONS

The biological variation and sigma-metrics models were demonstrated to be suitable for setting and evaluating quality targets within and between laboratories. The sigma-metrics model is more flexible, as both the TAE and the risk of failure can be adjusted to the situation-for example, requirements related to diagnosis/monitoring or international authorities. With the aim of reaching (inter)national consensus on advice regarding quality targets for Hb A1c, the Task Force suggests the sigma-metrics model as the model of choice, with default values of 5 mmol/mol (0.46%) for TAE and risk levels of 2σ and 4σ for routine laboratories and laboratories performing clinical trials, respectively. These goals should serve as a starting point for discussion with international stakeholders in the field of diabetes.