PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of diacareAmerican Diabetes AssociationSubscribeSearchDiabetes Care Journal
 
Diabetes Care. 2011 April; 34(4): 960–967.
Published online 2011 March 21. doi:  10.2337/dc10-1945
PMCID: PMC3064058

Nontraditional Markers of Glycemia

Associations with microvascular conditions
Elizabeth Selvin, PHD, MPH,1,2,3 Lesley M.A. Francis, MSC,1 Christie M. Ballantyne, MD,4 Ron C. Hoogeveen, PHD,4 Josef Coresh, MD, PHD,1,2,3 Frederick L. Brancati, MD, MHS,1,2,3 and Michael W. Steffes, MD, PHD5

Abstract

OBJECTIVE

To compare the associations of nontraditional (fructosamine, glycated albumin, 1,5-anhydroglucitol [1,5-AG]) and standard (fasting glucose, HbA1c) glycemic markers with common microvascular conditions associated with diabetes mellitus.

RESEARCH DESIGN AND METHODS

We conducted a cross-sectional study of 1,600 participants (227 with a history of diabetes and 1,323 without) from the Atherosclerosis Risk in Communities (ARIC) Study, a community-based population. We conducted logistic regression analyses of the associations of diabetes-specific tertiles of fructosamine, glycated albumin, 1/(1,5-AG), fasting glucose, and HbA1c with prevalence of chronic kidney disease, albuminuria, and retinopathy after adjustment for demographic, clinical, and lifestyle variables.

RESULTS

We observed significant positive trends in the associations of each marker with albuminuria and retinopathy, even after accounting for demographic, clinical, and lifestyle factors (all P trends <0.05). The associations with chronic kidney disease were similar in direction but were only significant for higher glycated albumin (P trend = 0.005), fructosamine (P trend = 0.003), and HbA1c (P trend = 0.005) values. After further adjustment for HbA1c, glycated albumin and fructosamine remained significantly or borderline significantly associated with the microvascular outcomes.

CONCLUSIONS

In cross-sectional analyses, two serum markers of glycemia—glycated albumin and fructosamine—are as, or more strongly, associated with microvascular conditions as HbA1c. These markers may be useful in settings where whole blood is not available. Whether they might complement or outperform HbA1c in terms of long-term predictive value requires further investigation.

HbA1c is the gold-standard measure for assessment of glycemic control and has recently been recommended for use in the diagnosis of diabetes (1). HbA1c results from the glycation of hemoglobin in erythrocytes and represents long-term (2–3 months) glycemia. Nonetheless, HbA1c has important limitations (1), and it is possible that nontraditional serum markers of glycemia, such as fructosamine, glycated albumin, and 1,5-anhydroglucitol (1,5-AG), may have added clinical utility. Glycated albumin and fructosamine reflect the modification of serum proteins by glucose and are markers of endogenous glucose exposure over the prior 2 to 4 weeks, i.e., extending beyond the half-life of albumin and some other serum proteins. 1,5-AG is a marker of glycemia-induced glycosuria, since reabsorption of filtered 1,5-AG in the proximal tubule is competitively inhibited by glucose (2,3). Lower serum 1,5-AG reflects high circulating glucose and the occurrence of glycosuria over the previous 1 to 2 weeks (3,4). These serum markers may be useful as adjuncts to HbA1c to provide information on short-term (e.g., 2–4 weeks) glycemic control and glycemic excursions, and/or for monitoring glycemic control when interpretation of HbA1c is problematic (e.g., in the presence of hemoglobinopathies, iron deficiency, and other anemias). However, few studies have examined the association of serum glycemic markers with complications (5). The objective of this study was to characterize and compare the associations of nontraditional (fructosamine, glycated albumin, 1,5-AG) and standard (fasting glucose, HbA1c) glycemic markers with common microvascular conditions associated with diabetes in a general population.

RESEARCH DESIGN AND METHODS

Study population

We conducted a cross-sectional study of participants from the Atherosclerosis Risk in Communities (ARIC) Study who participated in the ARIC Carotid MRI (CARMRI) substudy. The ARIC Study is an ongoing prospective cohort study of 15,792 black and white adults originally enrolled between 1987 and 1989 (6). Just over 2,000 participants from the original cohort, now aged 60–84 years, were recruited into the CARMRI substudy in 2004 and 2005 using a stratified sampling plan (7). In addition to the MRI examination, trained technicians performed a comprehensive clinical examination, obtained blood specimens, and conducted an interview to obtain information on health status and risk factors. Our study sample was limited to 1,600 participants (227 with a history of diabetes and 1,323 without) after excluding those who fasted less than 8 h (N = 20) or who were missing variables of interest (N = 402). An additional 51 participants had missing or ungradable retinal photographs and were further excluded from our analyses of retinopathy.

Institutional review boards at each clinical site approved the study protocol, and written informed consent was obtained from all participants.

Glycemic markers

HbA1c was measured from whole-blood samples as part of the original CARMRI protocol using the Tina-quant II method (Roche Diagnostics, Basel, Switzerland) implemented on a Roche Hitachi 911 Analyzer. This method is aligned to the Diabetes Control and Complications Trial assay. In 2009, we measured glycated albumin (Asahi Kasei Lucica GA-L; Asahi Kasei Pharma Corporation, Tokyo, Japan)—expressed as a percentage of total serum albumin, fructosamine (Roche Diagnostics), and 1,5-AG (GlycoMark, Winston-Salem, NC) from stored serum specimens using a Roche Modular P800 system. The interassay coefficients of variation (CVs) were 2.7% for glycated albumin, 3.7% for fructosamine, and 4.8% for 1,5-AG.

Outcomes

We focused on three microvascular conditions: chronic kidney disease, albuminuria, and retinopathy. Serum creatinine was measured using a Roche enzymatic assay, calibrated by the manufacturer to be traceable to reference method procedures (standard creatinine). The glomerular filtration rate (GFR) was estimated using the four-variable Modification of Diet in Renal Disease (MDRD) Study formula re-expressed to use standard creatinine and reported in milliliters per minute per 1.73 m2 (810). People with estimated GFR (eGFR) below 60 mL/min/1.73 m2 were considered to have chronic kidney disease (11). Urine albumin and creatinine were measured from random spot urine collected at the CARMRI visit using a clean-catch technique and sterile containers. We defined albuminuria as a urine albumin-to-creatinine ratio of 30 mg/g or higher and conducted sensitivity analyses excluding those people with macroalbuminuria (albumin-to-creatinine ratio ≥300 mg/g). Retinal photographs were taken following a standardized protocol that has been previously documented (12). Briefly, after 5 min of dark adaptation, a nonmydriatic 45-degree retinal photograph centered on the optic disc and macula was taken of one randomly selected eye. Trained readers masked to participant information evaluated each of the photographs. We defined retinopathy (moderate to severe) as a severity score greater than or equal to 35 according to a modification of the Airlie House classification system, as used in the Early Treatment Diabetic Retinopathy Study (ETDRS) (12,13).

Other variables of interest

Other measurement protocols in ARIC CARMRI were identical to those implemented in the original ARIC Study (7). Blood samples were assayed for total and high-density lipoprotein cholesterol, glucose, and high-sensitivity C-reactive protein using conventional techniques. BMI was computed from measured height and weight. Information on cigarette smoking and alcohol consumption was elicited during the interview. Resting systolic blood pressure (average of two readings) was measured using a random-zero sphygmomanometer. Participants were asked to bring current medications to the visit, and information on cholesterol- and blood pressure-lowering medications was also obtained during the interview. Diabetes history was determined by use of glucose-lowering medications or a self-reported physician diagnosis of diabetes. Previous history of coronary heart disease included a reported history of coronary heart disease and/or an adjudicated coronary heart disease event during active surveillance up to the CARMRI visit (14).

Statistical analysis

Characteristics of the study population were calculated overall and by history of diagnosed diabetes. We also compared mean values of each glycemic marker by categories of important risk factors separately in people with and without a history of diagnosed diabetes. We used multivariable logistic regression models to assess the independent association of each glycemic marker with microvascular conditions: chronic kidney disease, albuminuria, and retinopathy. For comparability, we divided the population into diabetes-specific tertiles of each glycemic marker. Because 1,5-AG is lowered while the other glycemic markers are increased in the setting of hyperglycemia, we transformed 1,5-AG to 1/1,5-AG for consistency in interpretation of the diabetes-specific tertiles (the ranking of individuals is unchanged by inverse transformation). Model 1 was adjusted for age (in years), sex, education level (less than high school; high school or equivalent; some college or higher), family history of diabetes, previous history of coronary heart disease, average systolic blood pressure (in mmHg), use of blood pressure medication, use of cholesterol-lowering medication, total cholesterol concentration (in mg/dL), HDL cholesterol concentration (in mg/dL), smoking status (current; former; never), BMI (in kg/m2), and high-sensitivity C-reactive protein concentration (in mg/L). Because there is ongoing debate regarding the need for correction of fructosamine assays for serum albumin (15), models of fructosamine were additionally adjusted for serum albumin concentration (in g/dL). Model 2 was adjusted for all variables in model 1 plus HbA1c (in percentages). All analyses were weighted by the inverse of the sample fractions in the study sampling strata using methods for the analysis of complex sample survey design data (7).

All reported P values are two-sided, and P values <0.05 were considered statistically significant.

RESULTS

The prevalence estimates for each the microvascular conditions in people with a history of diabetes were as follows: 27.7% had chronic kidney disease, 23.0% had albuminuria (4.1% macroalbuminuria), and 11.4% had retinopathy. In people without a history of diabetes, the corresponding prevalence estimates were 18.1% for chronic kidney disease, 10.4% for albuminuria (0.9% macroalbuminuria), and 2.8% for retinopathy. Demographic and clinical variables also differed substantially by diabetic status (Table 1). People with a history of diagnosed diabetes were more likely to be men and African American and have a high-school education or less and a family history of diabetes compared with people in the study sample with no history of diabetes. People with diagnosed diabetes also had poorer cardiovascular risk profiles and were less likely to be current smokers.

Table 1
Participant characteristics overall and by diabetes history

Mean levels of the different glycemic markers differed substantially by diabetic status (Tables 2 and and3).3). The mean glycated albumin, fructosamine, 1,5-AG, HbA1c, and fasting glucose values in people without a history of diagnosed diabetes were 13.6%, 230.1 μmol/L, 17.9 μg/mL, 5.6%, and 102.7 mg/dL, respectively. In people with diabetes, the corresponding means were 17.8%, 274.3 μmol/L, 12.6 μg/mL, 6.7%, and 140.1 mg/dL, respectively. Table 2 reveals substantial differences in the associations of each marker with basic demographic and clinical characteristics. In people without diabetes, glycated albumin was significantly higher and 1,5-AG was lower in people aged 65 years and older compared with people <65 years of age; fructosamine, HbA1c, and fasting glucose did not differ by age group. Whereas fructosamine and glycated albumin appeared similar by sex, men had significantly higher 1,5-AG, higher HbA1c, and higher fasting glucose. BMI was significantly associated with all markers but not in the expected direction for glycated albumin or fructosamine, which were both lower, and for 1,5-AG, which was higher, at higher BMI levels. By contrast, HbA1c and fasting glucose were both significantly higher at higher BMI levels. Levels of glycated albumin and fructosamine were also significantly higher in people who have never smoked compared with former or current smokers. HbA1c and fasting glucose were not significantly different across categories of smoking. Substantial racial differences were seen across all markers in both people with and without diabetes, with African Americans having higher levels of glycemia as indicated by each marker, although the results were not statistically significant for 1,5-AG in people with or without diabetes. Fasting glucose was higher, but not statistically significantly so, in diabetic African Americans compared with whites (P value = 0.13). In these unadjusted analyses, we observed associations between some of the glycemic markers and prevalent chronic kidney disease, albuminuria, and retinopathy, but these results were variable across the different measures of hyperglycemia (Tables 2 and and33).

Table 2
Mean levels of glycemic markers by demographic and clinical characteristics in participants without a history of diabetes (n = 1,323)
Table 3
Mean levels of glycemic markers by demographic and clinical characteristics in participants with diagnosed diabetes (n = 277)

In our adjusted logistic regression models of microvascular outcomes in the total population comparing diabetes-specific tertiles of each glycemic marker, we observed significant positive trends in the associations of each marker with albuminuria and retinopathy (Fig. 1 and Supplementary Table 1), even after accounting for demographic, clinical, and lifestyle factors. The associations with chronic kidney disease were similar in magnitude and direction but not significant for all markers: glycated albumin (P trend = 0.005), fructosamine (P trend = 0.003), and HbA1c (P trend = 0.005) were significantly and positively associated with the presence of chronic kidney disease, but 1/1,5-AG (P trend = 0.72) and fasting glucose (P trend = 0.66) were not. After further adjustment for HbA1c in these models (Supplementary Fig. 1 and Supplementary Table 2), we observed significant trends in the associations of glycated albumin with retinopathy (P trend = 0.01) and borderline significant trends for chronic kidney disease (P trend = 0.05) and albuminuria (P trend = 0.07). After adjustment for HbA1c, positive trends for fructosamine also remained significant for chronic kidney disease (P trend = 0.03) and retinopathy (P trend = 0.02) and borderline significant for albuminuria (P trend = 0.05). 1/1,5-AG remained significantly positively associated with albuminuria (P trend = 0.04) but not with chronic kidney disease (P trend = 0.63) or retinopathy (P trend = 0.42). Overall trends for fasting glucose were not significant for any outcome after adjustment for HbA1c (all P trends >0.3); however, the lowest tertile of fasting glucose in people with diagnosed diabetes was significantly associated with chronic kidney disease and albuminuria. HbA1c, by contrast, was no longer statistically significantly associated with any of these outcomes in these fully adjusted models (all P values >0.05).

Figure 1
Adjusted odds ratios (95% CI) for microvascular conditions by diabetes-specific tertiles of each glycemic marker; odds ratios are adjusted for age, sex, education level, family history of diabetes, previous coronary heart disease, use of blood pressure ...

CONCLUSIONS

We compared nontraditional serum markers of glycemia (glycated albumin, fructosamine, and 1,5-AG) to the standard measures used in clinical practice (fasting glucose and HbA1c) in their associations with microvascular conditions. We observed intriguing differences in the associations of each glycemic marker with basic demographic and clinical characteristics. To the extent that cross-sectional risk factor associations differ, this may indicate that the different glycemic markers contribute independent clinical information and may enhance the prognostic value of standard glycemic markers. Indeed, we observed qualitative differences in the associations of the various glycemic markers with smoking status and BMI. There is evidence that both smoking and higher levels of adiposity can lead to a state of increase oxidative stress because of increased reactive oxygen species (16). Our data may indicate that circulating concentrations of glycated albumin and fructosamine are differentially affected by these states of oxidant stress compared with traditional glycemic markers. It is also possible that glycated albumin, fructosamine, and 1,5-AG are more strongly affected by postprandial glycemic excursions compared with HbA1c, contributing to the observed differences (17,18).

After adjustment for confounding factors, we found that glycated albumin, fructosamine, and HbA1c were similarly positively associated with prevalent chronic kidney disease, albuminuria, and retinopathy. Fasting glucose and 1,5-AG were associated with albuminuria and retinopathy but not chronic kidney disease. The associations of glycated albumin and fructosamine with microvascular outcomes were evident even after adjustment for HbA1c, suggesting that these serum markers of glycemic control may contribute independent risk information. The less robust results for fasting glucose, particularly among people with a diagnosis of diabetes in the upper two tertiles of the glycemic markers, may partially reflect that nearly all people with diagnosed diabetes reported current use of glucose-lowering medication(s).

Fructosamine is often used in clinical practice to monitor glycemic control in people with conditions that interfere with the interpretation or measurements of HbA1c, such as the presence of some hemoglobin variants, certain anemias, dialysis treatment, or other conditions that cause hemolysis or otherwise alter erythrocyte turnover. The colorometric process of the fructosamine assay is challenging to perform, and there is ongoing debate regarding the need for correction of fructosamine assays for serum albumin concentrations (15,19). For this reason, we adjusted all fructosamine analyses for serum albumin concentration. An additional general concern regarding fructosamine is the fluctuating concentrations of other serum proteins that may affect the assay result. Our study demonstrates excellent reliability of this fructosamine assay with an interassay CV = 3.7% and significant associations of fructosamine with microvascular outcomes. The glycated albumin assay examined here is a newer automated assay not yet approved for clinical use in the U.S. but which also showed excellent laboratory performance, interassay CV = 2.7%. In this assay, glycated albumin is expressed as a percentage of total serum albumin (20), simplifying the interpretation of the result. Indeed, additional adjustment for serum albumin in our analyses of glycated albumin and microvascular outcomes did not appreciably alter our results (data not shown). It has also been suggested that glycated albumin itself, beyond its role as marker of hyperglycemia, may contribute directly to the development of complications (21), including diabetic nephropathy (22,23).

1,5-AG is a novel marker of glycemia, reflecting a nonglycation-dependent biological process: it is thought to reflect hyperglycemic excursions over the past 1 to 2 weeks (4). Urinary excretion of 1,5-AG is accelerated in the setting of hyperglycemia and high circulating concentrations of glucose (exceeding the renal threshold) will cause serum 1,5-AG concentrations to fall (24). The attractiveness of 1,5-AG is that it may capture additional information on glycemic excursions not reflected in fasting glucose, HbA1c, fructosamine, or glycated albumin values (17). However, it is unclear whether concentrations of 1,5-AG may be altered in the presence of moderately impaired kidney function or albuminuria, raising questions regarding reverse causality.

Limitations that should be considered in the interpretation of these data include the cross-sectional design; we could not determine the temporality of the observed associations, and we had only a limited number of people with known diabetes (N = 277). Sample size limitations excluded the possibility of comprehensive examination of other subgroups. The direct comparison between results for glycated albumin versus those for fructosamine remains challenging. To our knowledge, there is no reference method procedure for glycated albumin or for those measurements related to glycation of proteins circulating in blood. Thus, the fructosamine assay (a colorimetric assay based on ketoamines reducing nitrotetrazolium-blue) does not directly relate to the measure of glycated albumin (an enzymatic method using ketoamine oxidase and an albumin-specific protease), and differences in results across these measurements are difficult to interpret in the context of an epidemiologic study. Important strengths of this study include the comparison of a panel of novel serum markers with those that are used in standard practice, the community-based biracial sample of people with and without diabetes, which allowed us to assess these epidemiologic associations across the full range of glycemia. Additional advantages to conducting this study nested within the ARIC cohort included the rigorous measurement of known risk factors for diabetes using standardized protocols and assessment of multiple microvascular conditions. We observed excellent laboratory performance for all assays.

Physicians typically use multiple biomarkers to assess the metabolic status of their patients, but the additional clinical utility of serum glycemic markers beyond standard measures of glycemia is unclear. In our cross-sectional analyses, two serum markers of glycemia—glycated albumin and fructosamine—were as strongly, or more strongly, associated with microvascular conditions as HbA1c. Our results suggest that serum glycemic markers, particularly glycated albumin and/or fructosamine, may add important clinical information for the identification of people at risk for microvascular conditions and possibly the management of diabetes. Measurement of HbA1c requires a whole-blood sample; sometimes relatively labor-intensive assay methodologies and concerns have been raised about the performance of HbA1c in certain subpopulations (25). The markers examined here can be measured reliably in serum using standard, automated methods and may be useful in settings where whole blood is not available. Whether serum glycemic markers might complement or outperform HbA1c in terms of long-term predictive value requires further investigation.

Supplementary Material

Supplementary Data:

Acknowledgments

This research was supported by the National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Grants R21-DK-080294 and K01-DK-076595 (to E.S.). The ARIC Study was carried out as a collaborative study supported by the NIH National Heart, Lung, and Blood Institute Contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022. F.L.B. was supported by NIH/NIDDK Grant K24-DK-62222 and by the Johns Hopkins Diabetes Research and Training Center, NIDDK Grant P60-DK-079637. Reagents for the glycated albumin assays were provided by the Asahi Kasei Corporation.

No potential conflicts of interest relevant to this article were reported.

E.S. collected and analyzed the data and wrote the article. L.M.A.F. analyzed the data and reviewed the article. C.M.B. reviewed the article and contributed to the discussion. R.C.H., J.C., and F.L.B. reviewed and edited the article and contributed to the discussion. M.W.S. collected the data and reviewed and edited the article and contributed to the discussion.

The authors thank the staff and participants of the ARIC Study for their important contributions.

Footnotes

This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc10-1945/-/DC1.

References

1. American Diabetes Association Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33(Suppl. 1):S62–S69. [PMC free article] [PubMed]
2. Buse JB, Freeman JL, Edelman SV, Jovanovic L, McGill JB. Serum 1,5-anhydroglucitol (GlycoMark): a short-term glycemic marker. Diabetes Technol Ther 2003;5:355–363. [PubMed]
3. Yamanouchi T, Minoda S, Yabuuchi M, et al. Plasma 1,5-anhydro-d-glucitol as new clinical marker of glycemic control in NIDDM patients. Diabetes 1989;38:723–729. [PubMed]
4. Stettler C, Stahl M, Allemann S, et al. Association of 1,5-anhydroglucitol and 2-h postprandial blood glucose in type 2 diabetic patients. Diabetes Care 2008;31:1534–1535. [PMC free article] [PubMed]
5. Schalkwijk CG, Chaturvedi N, Twaafhoven H, van Hinsbergh VW, Stehouwer CD, EUCLID Study Group Amadori-albumin correlates with microvascular complications and precedes nephropathy in type 1 diabetic patients. Eur J Clin Invest 2002;32:500–506. [PubMed]
6. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol 1989;129:687–702. [PubMed]
7. Wagenknecht L, Wasserman B, Chambless L, et al. Correlates of carotid plaque presence and composition as measured by MRI: the Atherosclerosis Risk in Communities Study. Circ Cardiovasc Imaging 2009;2:314–322. [PMC free article] [PubMed]
8. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D, Modification of Diet in Renal Disease Study Group A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 1999;130:461–470. [PubMed]
9. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007;298:2038–2047. [PubMed]
10. Levey AS, Coresh J, Greene T, et al. Chronic Kidney Disease Epidemiology Collaboration Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006;145:247–254. [PubMed]
11. National Kidney Foundation K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39(Suppl. 1):S1–S266. [PubMed]
12. Hubbard LD, Brothers RJ, King WN, et al. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology 1999;106:2269–2280. [PubMed]
13. Prior MJ, Prout T, Miller D, Ewart R, Kumar D, The ETDRS Research Group C-peptide and the classification of diabetes mellitus patients in the Early Treatment Diabetic Retinopathy Study. Report number 6. Ann Epidemiol 1993;3:9–17. [PubMed]
14. White AD, Folsom AR, Chambless LE, et al. Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: methods and initial two years’ experience. J Clin Epidemiol 1996;49:223–233. [PubMed]
15. Goldstein DE, Little RR, Lorenz RA, Malone JI, Nathan DM, Peterson CM, American Diabetes Association Tests of glycemia in diabetes. Diabetes Care 2003;26(Suppl. 1):S106–S108. [PubMed]
16. de Ferranti S, Mozaffarian D. The perfect storm: obesity, adipocyte dysfunction, and metabolic consequences. Clin Chem 2008;54:945–955. [PubMed]
17. Dungan KM, Buse JB, Largay J, et al. 1,5-Anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes Care 2006;29:1214–1219. [PubMed]
18. Koga M, Kasayama S. Clinical impact of glycated albumin as another glycemic control marker. Endocr J 2010;57:751–762. [PubMed]
19. Lin MJ, Hoke C, Ettinger B, Coyne RV. Technical performance evaluation of BM/Hitachi 747-200 serum fructosamine assay. Clin Chem 1996;42:244–248. [PubMed]
20. Kohzuma T, Koga M. Lucica GA-L glycated albumin assay kit: a new diagnostic test for diabetes mellitus. Mol Diagn Ther 2010;14:49–51. [PubMed]
21. Hattori Y, Suzuki M, Hattori S, Kasai K. Vascular smooth muscle cell activation by glycated albumin (Amadori adducts). Hypertension 2002;39:22–28. [PubMed]
22. Doublier S, Salvidio G, Lupia E, et al. Nephrin expression is reduced in human diabetic nephropathy: evidence for a distinct role for glycated albumin and angiotensin II. Diabetes 2003;52:1023–1030. [PubMed]
23. Cohen MP, Chen S, Ziyadeh FN, et al. Evidence linking glycated albumin to altered glomerular nephrin and VEGF expression, proteinuria, and diabetic nephropathy. Kidney Int 2005;68:1554–1561. [PubMed]
24. Yamanouchi T, Moromizato H, Shinohara T, Minoda S, Miyashita H, Akaoka I. Estimation of plasma glucose fluctuation with a combination test of hemoglobin A1c and 1,5-anhydroglucitol. Metabolism 1992;41:862–867. [PubMed]
25. Bloomgarden ZT. A1C: recommendations, debates, and questions. Diabetes Care 2009;32:e141–e147. [PubMed]

Articles from Diabetes Care are provided here courtesy of American Diabetes Association