A different approach to analyzing age-related HbA1c values in non-diabetic subjects

Hemoglobin A1c Levels Associated with Age and Gender in Taiwanese Adults without Prior Diagnosis with Diabetes

Several studies have reported that Hemoglobin A1c (HbA1c) levels increase with age for people without diabetes. However, HbA1c levels associated with age and gender have not been well investigated for Taiwanese adults. The objective of this study was to investigate the sex-specific association between HbA1c levels and age for Taiwanese adults without diabetes. The data were collected from the Taiwan Biobank database with inclusive criteria being participants without diabetes. The association between HbA1c values and age was conducted by linear regression analysis, HbA1c values between sexes were compared by two-sample t-test, and HbA1c levels among age groups were compared using one-way ANOVA. The results showed that HbA1c levels were positively correlated with age, and the levels for males were significantly higher than for females among all participants. However, there was no significantly positive correlation between HbA1c levels and age in males for age group of 50–70 years. The levels of males were significantly higher than females for age groups of 30–39 and 40–49 years. There were significant differences in HbA1c levels among age groups for all participants, males, and females except for the two age groups of 50–59 and 60–70 years in males. Age and gender were important factors affecting HbA1c levels. Our results suggested that the HbA1c cut-point levels for the diagnosis of diabetes should vary by age and gender.

Translating the HbA1c assay into estimated average glucose values in children and adolescents with type 1 diabetes mellitus

OBJECTIVE

The A1c assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per decilitre or millimoles per litter) as well as by using continuous glucose monitoring systems (CGMS). We found a mathematical relationship between A1c and average glucose (AG) levels measured by CGMS over 5 days and determined the correlation between the variable CGMS parameters and HbA1c in 50 children with type 1 diabetes mellitus (DM-1) on MDI therapy.

RESEARCH DESIGN AND METHODS

A total of 50 diabetic children randomly selected from a cohort of children with DM-1 were included in the analyses. A1c levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 5 days recorded by CGMS. AG was calculated by combining weighted results from 5 days of continuous glucose monitoring performed before measuring HbA1c, with 3-5 point daily self-monitoring of capillary (fingerstick) glucose.

RESULTS

Linear regression analysis between the A1c and AG values provided the tightest correlations HbA1c=0.0494 MG- 2E-14, R2=0.90, P<0.0001), allowing calculation of an estimated average glucose (eAG) for A1c values.

CONCLUSIONS

Our study showed a linear relationship between HbA1C and AG values measured by CGMS for 5 days before HbA1c measurement. The AG can be easily calculated using a formula derived from linear regression analysis of HbA1c data obtained in our diabetic children.

Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes

BACKGROUND

Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect.

METHODS

Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8).

RESULTS

The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552*θ1 + 0.007645*θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well.

CONCLUSIONS

The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.

Accuracy and robustness of dynamical tracking of average glycemia (A1c) to provide real-time estimation of hemoglobin A1c using routine self-monitored blood glucose data

BACKGROUND

Laboratory hemoglobin A1c (HbA1c) assays are typically done only every few months. However, self-monitored blood glucose (SMBG) readings offer the possibility for real-time estimation of HbA1c. We present a new dynamical method tracking changes in average glycemia to provide real-time estimation of A1c (eA1c).

MATERIALS AND METHODS

A new two-step algorithm was constructed that includes: (1) tracking fasting glycemia to compute base eA1c updated with every fasting SMBG data point and (2) calibration of the base eA1c trace with monthly seven-point SMBG profiles to capture the principal components of blood glucose variability and produce eA1c. A training data set (n=379 subjects) was used to estimate model parameters. The model was then fixed and applied to an independent test data set (n=375 subjects). Accuracy was evaluated in the test data set by computing mean absolute deviation (MAD) and mean absolute relative deviation (MARD) of eA1c from reference HbA1c, as well as eA1c-HbA1c correlation.

RESULTS

MAD was 0.50, MARD was 6.7%, and correlation between eA1c and reference HbA1c was r=0.76. Using an HbA1c error grid plot, 77.5% of all eA1c fell within 10% from reference HbA1c, and 97.9% fell within 20% from reference.

CONCLUSIONS

A dynamical estimation model was developed that achieved accurate tracking of average glycemia over time. The model is capable of working with infrequent SMBG data typical for type 2 diabetes, thereby providing a new tool for HbA1c estimation at the patient level. The computational demands of the procedure are low; thus it is readily implementable into home SMBG meters. Real-time HbA1c estimation could increase patients' motivation to improve diabetes control.

A history of HbA1c through Clinical Chemistry and Laboratory Medicine

HbA(1c) was discovered in the late 1960s and its use as marker of glycemic control has gradually increased over the course of the last four decades. Recognized as the gold standard of diabetic survey, this parameter was successfully implemented in clinical practice in the 1970s and 1980s and internationally standardized in the 1990s and 2000s. The use of standardized and well-controlled methods, with well-defined performance criteria, has recently opened new directions for HbA(1c) use in patient care, e.g., for diabetes diagnosis. Many reports devoted to HbA1c have been published in Clinical Chemistry and Laboratory Medicine (CCLM) journal. This review reminds the major steps of HbA(1c) history, with a special emphasis on the contribution of CCLM in this field.

HbA1c for screening and diagnosis of diabetes mellitus

HbA1c has become the gold standard for monitoring glycemic control in patients with diabetes mellitus. The use of this test has been expanded to diagnose and screen for diabetes mellitus with the endorsement of influential diabetes societies and the World Health Organization. The literature on the use of HbA1c for the diagnosis and screening of diabetes mellitus was critically examined. There is substantial recent literature on this topic with strong advocates for the use of HbA1c to diagnose and screen for diabetes and equally strong detractors for its use. Advocates of the use of HbA1c cite challenges in respect of patient compliance and the analysis of glucose and inconsistency of diagnosis with glucose-based diabetes diagnosis with the elimination or reduction in these challenges in HbA1c-based diagnosis. Detractors of its use cite increased cost, concerns about the availability of HbA1c testing, and the influence of demographic and clinical factors on HbA1c results that make the use of a single-threshold values questionable for different ethnic and age groups. Despite the recommendation of many international diabetes societies that HbA1c be used for screening and diagnosis of diabetes mellitus, there is a wide divergence of opinion on this use.

Alterations in HbA(1c) with advancing age in subjects with normal glucose tolerance: Chandigarh Urban Diabetes Study (CUDS)

OBJECTIVE

To study the alterations in HbA(1c) with advancing age in subjects with normal glucose tolerance.

METHODS

Community-based cross-sectional study involving 2368 subjects aged ≥ 20 years from Chandigarh, India. All the subjects underwent an oral glucose tolerance test with 75 g anhydrous glucose and were classified as having normal glucose tolerance, pre-diabetes or diabetes according to World Health Organization 1999 criteria. HbA(1c) was measured on a National Glycohemoglobin Standardization Program-certified Bio-Rad D-10 system and the data were available for 1972 subjects.

RESULTS

Out of 1972 subjects, 1317 (67%) subjects had normal glucose tolerance. There was a significant positive correlation between mean HbA(1c) and age in these subjects (r = 0.308, P(trend) < 0.001). The increase in HbA(1c) with each advancing year was 0.01% above the age of 20 years and corrected HbA(1c) (%) for age was 5.09 + 0.01 (age). The 95th percentile of HbA(1c) exceeded 6.5% (48 mmol/mol) (the American Diabetes Association cut-off for diagnosis of diabetes) in subjects aged ≥ 70 years. A significantly higher number (6.5%, 21/325) of subjects had HbA(1c) of ≥ 6.5% (48 mmol/mol) in those above the age of 50 years compared with those below the age of 50 years (1.7%, 17/992) in the group with normal glucose tolerance (P < 0.001). On multivariate regression analysis, after adjusting for BMI, fasting plasma glucose and 2-h plasma glucose post-glucose load, the correlation of HbA(1c) with age still remained significant (r = 0.241, P < 0.01).

CONCLUSION

HbA(1c) increases with advancing age independent of glycaemia, suggesting caution when seeking to achieve the recommended HbA(1c) targets in the elderly population.

Effects of AEDs on biomarkers in people with epilepsy: CRP, HbA1c and eGFR

The standardised mortality ratio in people with epilepsy is raised to between 2 and 3 compared with the general population. Some biomarker levels, including higher C-reactive protein (CRP), higher glycosylated haemoglobin (HbA1c) and lower estimated glomerular filtration rate (eGFR), are associated with an increase risk of premature mortality. These biomarkers were measured in 125 people with refractory epilepsy to estimate the potential effect of antiepileptic drug (AED) use on these markers. Multiple regression analysis showed that valproate (N=50) use was associated with 55% lower mean CRP concentrations and higher mean eGFR values; and phenytoin (N=32) use with 4% lower mean HbA1c values. These potentially represent health markers improved by AEDs. On the other hand, lamotrigine use (N=48) was associated with 13% lower mean eGFR and this may represent a negative effect on a health marker. These preliminary observations clearly require further controlled studies ideally in people on AED monotherapy.

Does glycated hemoglobin have clinical significance in ischemic stroke patients?

OBJECTIVES

It has been suggested that patients with an elevated hemoglobin A1c (HbA1c) level have an increased risk of cardiovascular disease regardless of the presence of diabetes. However, an association between HbA1c and stroke has not yet been determined. In this study, our purpose was to examine whether HbA1c was independently associated with various types of cerebral vascular lesions in stroke patients.

METHODS

A consecutive series of acute ischemic stroke patients were included for this analysis from October, 2002, to March, 2006. HbA1c was examined on admission, and MR imaging was performed for analysis of large artery diseases (LADs) and small artery diseases (SADs). Symptomatic or asymptomatic LAD was diagnosed by MR angiography, and SAD was classified as leukoaraiosis, microbleeds, or old lacunar infarctions.

RESULTS

A total of 639 stroke patients were analyzed (diabetics, n=247; non-diabetics, n=392). There was no relationship between the level of HbA1c and any type of cerebrovascular lesion in the non-diabetic patients. In contrast, HbA1c showed a significant negative association with symptomatic LAD and leukoaraiosis in the diabetic patients using univariate analysis (p=0.01 and p<0.05, respectively). These associations did not remain significant, however, after adjustment for age and hypertension. This was, in part, because the HbA1c level in our diabetic population decreased gradually with age (p=0.03).

CONCLUSIONS

Our results indicate that HbA1c is not associated with risk for various types of cerebrovascular lesions in ischemic stroke patients. The negative association between age and HbA1c in diabetic patients should be further investigated.

Effect of aging on A1C levels in individuals without diabetes: evidence from the Framingham Offspring Study and the National Health and Nutrition Examination Survey 2001-2004

OBJECTIVE

Although glycemic levels are known to rise with normal aging, the nondiabetic A1C range is not age specific. We examined whether A1C was associated with age in nondiabetic subjects and in subjects with normal glucose tolerance (NGT) in two population-based cohorts.

RESEARCH DESIGN AND METHODS

We performed cross-sectional analyses of A1C across age categories in 2,473 nondiabetic participants of the Framingham Offspring Study (FOS) and in 3,270 nondiabetic participants from the National Health and Nutrition Examination Survey (NHANES) 2001-2004. In FOS, we examined A1C by age in a subset with NGT, i.e., after excluding those with impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT). Multivariate analyses were performed, adjusting for sex, BMI, fasting glucose, and 2-h postload glucose values.

RESULTS

In the FOS and NHANES cohorts, A1C levels were positively associated with age in nondiabetic subjects. Linear regression revealed 0.014- and 0.010-unit increases in A1C per year in the nondiabetic FOS and NHANES populations, respectively. The 97.5th percentiles for A1C were 6.0% and 5.6% for nondiabetic individuals aged <40 years in FOS and NHANES, respectively, compared with 6.6% and 6.2% for individuals aged >or=70 years (P(trend) < 0.001). The association of A1C with age was similar when restricted to the subset of FOS subjects with NGT and after adjustments for sex, BMI, fasting glucose, and 2-h postload glucose values.

CONCLUSIONS

A1C levels are positively associated with age in nondiabetic populations even after exclusion of subjects with IFG and/or IGT. Further studies are needed to determine whether age-specific diagnostic and treatment criteria would be appropriate.