IFCC recommendation on reporting results for blood glucose

Age and cystatin C in healthy adults: a collaborative study

BACKGROUND

Kidney function declines with age, but a substantial portion of this decline has been attributed to the higher prevalence of risk factors for kidney disease at older ages. The effect of age on kidney function has not been well described in a healthy population across a wide age spectrum.

METHODS

The authors pooled individual-level cross-sectional data from 18 253 persons aged 28-100 years in four studies: the Cardiovascular Health Study; the Health, Aging and Body Composition Study; the Multi-Ethnic Study of Atherosclerosis and the Prevention of Renal and Vascular End-Stage Disease cohort. Kidney function was measured by cystatin C. Clinical risk factors for kidney disease included diabetes, hypertension, obesity, smoking, coronary heart disease, cerebrovascular disease, peripheral arterial disease and heart failure.

RESULTS

Across the age range, there was a strong, non-linear association of age with cystatin C concentration. This association was substantial, even among participants free of clinical risk factors for kidney disease; mean cystatin C levels were 46% higher in participants 80 and older compared with those <40 years (1.06 versus 0.72 mg/L, P < 0.001). Participants with one or more risk factors had higher cystatin C concentrations for a given age, and the age association was slightly stronger (P < 0.001 for age and risk factor interaction).

CONCLUSIONS

There is a strong, non-linear association of age with kidney function, even in healthy individuals. An important area for research will be to investigate the mechanisms that lead to deterioration of kidney function in apparently healthy persons.

Association between glycometabolic status in the acute phase and 2½ years after an acute coronary syndrome

OBJECTIVES

To evaluate the association between glycometabolic status in the acute phase and 21/2 years later in patients with acute coronary syndrome (ACS).

METHODS

Non-diabetic patients (n = 762) presenting with ACS were prospectively followed up for 21/2 years. Patients were stratified by admission plasma glucose (<6.1 mmol/l, 6.1 - 6.9 mmol/l and >or=7.0 mmol/l) and HbA1c (<or=4.5%, 4.6 - 5.4% and >or=5.5%). The predictive value of glucose levels >or= 7.0 mmol/l and HbA1c >or= 5.5% for glycometabolic disturbance (i.e. diabetes or impaired fasting glycaemia (IFG)) was analysed.

RESULTS

Of 762 patients, 13% had a diagnosis of diabetes and 16% had IFG at follow-up. The prevalence of glycometabolic disturbance at follow-up increased with increasing plasma glucose at admission, from 19% in patients with < 6.1 mmol/l to 42% in patients with >or= 7.0 mmol/l. Sixty-one percent of patients with HbA1c >or= 5.5% had glycometabolic disturbance after 21/2 years compared to only 25% of those with HbA1c < 5.5%.

CONCLUSION

Non-diabetic patients with ACS and hyperglycaemia are at high risk for developing glycometabolic disturbance. HbA1c may be an even stronger predictor of glycometabolic disturbance than plasma glucose.

The correlation of hemoglobin A1c to blood glucose

The understanding that hemoglobin A1c (HbA1c) represents the average blood glucose level of patients over the previous 120 days underlies the current management of diabetes. Even in making such a statement, we speak of "average blood glucose" as though "blood glucose" were itself a simple idea. When we consider all the blood glucose forms-arterial versus venous versus capillary, whole blood versus serum versus fluoride-preserved plasma, fasting versus nonfasting-we can start to see that this is not a simple issue. Nevertheless, it seems as though HbA1c correlates to any single glucose measurement. Having more than one measurement and taking those measurements in the preceding month improves the correlation further. In particular, by having glucose measurements that reflect both the relatively lower overnight glucose levels and measurements that reflect the postprandial peaks improves not only our ability to manage diabetes patients, but also our understanding of how HbA1c levels are determined. Modern continuous glucose monitoring (CGM) devices may take thousands of glucose results over a week. Several studies have shown that CGM glucose averages account for the vast proportion of the variation of HbA1c. The ability to relate HbA1c to average glucose may become a popular method for reporting HbA1c, eliminating current concerns regarding differences in HbA1c standardization. Hemoglobin A1c expressed as an average glucose may be more understandable to patients and improve not only their understanding, but also their ability to improve their diabetes management.

Overview on self-monitoring of blood glucose

The self-monitoring of blood glucose (SMBG), traditionally performed by "point-of-care" (POC) devices called portable glucose monitors (PGM) is now considered an integral part of managed care of diabetic patients, especially type 1 diabetics and those on insulin therapy. In patients with type 2 diabetes, SMBG can help to achieve a better glycaemic control, although there is not sufficient evidence to attest that strict monitoring in these patients is associated with an improved outcome. The outcome of several clinical studies, especially in diabetics on insulin therapy, has shown that SMBG plays a key role in preventing complications in the short, medium and long term. According to the current recommendations, SMBG is aimed to achieve and maintain glycaemic control, prevent and identify hypoglycaemia, prevent severe hyperglycaemia, adjust lifestyle changes and establish the need to begin treatment with insulin in gestational diabetes mellitus. However, as clearly highlighted by the American Diabetes Association (ADA) and the National Academy of Clinical Biochemistry (NACB), patients and healthcare personnel should be trained on the appropriate use of the device, as well as on the correct interpretation of data. Moreover, definite analytical targets and appropriate acceptance criteria for performance should be fulfilled before a new device is introduced in the hospital environment, or recommended to the patients. Performance limitations such as hematocrit extremes and analytical interferences should be clearly acknowledged by the operators, before taking test results for granted. The current article aims to review the current indications for SMGB and highlight the most important criteria for the appropriate use of PGMs.

Clinical Use and Pathogenetic Basis of Laboratory Tests for the Evaluation of Primary Arterial Hypertension

This review focuses on the laboratory biochemical tests that are useful in the diagnostic approach to the hypertensive patient. A "minimal" diagnostic laboratory work-up, including a small number of tests that are simple and relatively inexpensive, is first described. Because these tests provide basic information on the presence of major cardiovascular (CV) risk factors and target organ damage, and might give some clues to the presence of a secondary form of hypertension (HT), they should be performed on all patients presenting with HT. Other tests that are aimed at assessing the overall CV risk, a major determinant of prognosis that dictates the therapeutic strategy in the individual HT patient, are then discussed. They allow identification of major CV risk factors and associated clinical conditions which, if present, lead to a substantial change of therapeutic strategy. The role of C-reactive protein as a marker of atherosclerosis and its predictive value for CV events are also discussed. Finally, a section is devoted to tests that are currently confined to research purposes, such as markers of endothelial function including endothelin-1, homocysteine and genetic analysis.

Evaluation of the VIA Blood Chemistry Monitor for Glucose in Healthy and Diabetic Volunteers

BACKGROUND

Manual methods of blood glucose monitoring are labor-intensive, costly, prone to error, and expose the caregiver to blood. The VIA(R) blood chemistry monitor for glucose can automatically measure plasma glucose (PG) every 5 minutes for 72 hours using blood sampled from a peripheral vein/artery or a central vein.

METHODS

VIA performance was evaluated in eight normal and five type 1 diabetic (T1DM) subjects in 15 separate experiments. The VIA device was connected to a peripheral vein and reported a PG value every 5 minutes during each 510-minute experiment. Blood samples were collected manually every 10 minutes and assayed using a HemoCue(R) beta-glucose analyzer (HC). Whole blood HC measurements were corrected to PG values. Paired HC/VIA measurements (n = 717) were analyzed.

RESULTS

Mean PG was 90 +/- 14 and 96 +/- 12 mg/dl in normal subjects and 194 +/- 64 and 173 +/- 48 mg/dl in T1DM subject as measured by the HC and VIA, respectively. Clark error grid analysis revealed 86% points in zone A, 11% points in zone B, and 2% points in zone D. Linear regression analysis yielded the following equation: VIA = 0.732 x HC + 30.5 (r(2) = 0.954). Residual analysis revealed a glucose-dependent bias between the HC and the VIA. VIA data were transformed using the linear regression equation to correct for bias. After the correction, the mean absolute relative difference between the VIA and the HC was less than 10%, and 99.6% of data were in zones A and B. The VIA was able to sample blood automatically every 5 minutes for more than 8 hours in the laboratory setting. On average, the VIA reported glucose values for 94% of the samples it attempted to obtain.

CONCLUSIONS

This study demonstrated that the VIA blood chemistry monitor for glucose can reliably sample blood frequently for a prolonged period of time safely and effectively in diabetic and nondiabetic volunteers. Agreement between the two devices was the closest at normal glucose concentrations. After correcting for a glucose-dependent bias between the devices, the MARD was consistently less than 10% for all glucose ranges.

Plasma-equivalent glucose at the point-of-care: evaluation of Roche Accu-Chek Inform and Abbott Precision PCx glucose meters

BACKGROUND

Glucose testing at the bedside has become an integral part of the management strategy in diabetes and of the careful maintenance of normoglycemia in all patients in intensive care units. We evaluated two point-of-care glucometers for the determination of plasma-equivalent blood glucose.

METHODS

The Precision PCx and the Accu-Chek Inform glucometers were evaluated. Imprecision and bias relative to the Vitros 950 system were determined using protocols of the Clinical Laboratory Standards Institute (CLSI). The effects of low, normal, and high hematocrit levels were investigated. Interference by maltose was also studied.

RESULTS

Within-run precision for both instruments ranged from 2-5%. Total imprecision was less than 5% except for the Accu-Chek Inform at the low level (2.9 mmol/L). Both instruments correlated well with the comparison instrument and showed excellent recovery and linearity. Both systems reported at least 95% of their values within zone A of the Clarke Error Grid, and both fulfilled the CLSI quality criteria. The more stringent goals of the American Diabetes Association, however, were not reached. Both systems showed negative bias at high hematocrit levels. Maltose interfered with the glucose measurements on the Accu-Chek Inform but not on the Precision PCx.

CONCLUSIONS

Both systems showed satisfactory imprecision and were reliable in reporting plasma-equivalent glucose concentrations. The most stringent performance goals were however not met.

Accuracy of the 5-day FreeStyle Navigator Continuous Glucose Monitoring System: comparison with frequent laboratory reference measurements

OBJECTIVE

The purpose of this study was to compare the accuracy of measurements of glucose in interstitial fluid made with the FreeStyle Navigator Continuous Glucose Monitoring System with Yellow Springs Instrument laboratory reference measurements of venous blood glucose.

RESEARCH DESIGN AND METHODS

Fifty-eight subjects with type 1 diabetes, aged 18-64 years, were enrolled in a multicenter, prospective, single-arm study. Each subject wore two sensors simultaneously, which were calibrated with capillary fingerstick measurements at 10, 12, 24, and 72 h after insertion. Measurements from the FreeStyle Navigator system were collected at 1-min intervals and compared with venous measurements taken once every 15 min for 50 h over the 5-day period of sensor wear in an in-patient clinical research center. Periods of high rates of change of glucose were induced by insulin and glucose challenges.

RESULTS

Comparison of the FreeStyle Navigator measurements with the laboratory reference method (n = 20,362) gave mean and median absolute relative differences (ARDs) of 12.8 and 9.3%, respectively. The percentage in the clinically accurate Clarke error grid A zone was 81.7% and that in the in the benign error B zone was 16.7%. During low rates of change (< +/-1 mg x dl(-1) x min(-1)), the percentage in the A zone was higher (84.9%) and the mean and median ARDs were lower (11.7 and 8.5%, respectively).

CONCLUSIONS

Measurements with the FreeStyle Navigator system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 5 days of sensor wear (82.5% in the A zone on day 1 and 80.9% on day 5).

Probable blind spot in the International Diabetes Federation definition of metabolic syndrome

OBJECTIVES

The International Diabetes Federation (IDF) proposed a novel definition of the metabolic syndrome (MS) in 2005, which designated central obesity as mandatory. The new National Cholesterol Education Program (NCEP) version, announced by the American Heart Association and National Heart Lung and Blood Institute in October 2005, did not favor any of the five components. We set out to compare the cardiovascular profiles of patients cross-defined by these two definitions to shed light on the differential meanings of the two.

RESEARCH METHODS AND PROCEDURES

We analyzed data from 2608 non-institutionalized adults (> or =19 years old) in the National Nutrition and Health Survey in Taiwan, who had complete data for the five MS defining components. Both definitions adopted lower cut-points for fasting glucose and race-specific cut-points for waist circumference.

RESULTS

Under the IDF's and new NCEP's definitions, the MS prevalence was 6.2% and 11.6% in men and 12.6% and 16.5% in women, respectively. Although the two definitions had high agreement, IDF failed to pick up approximately 4% to 5% of people with more than three MS component disorders but a waist circumference less than the cut-point. Subjects whose physical conditions only satisfied NCEP's definition had similar or worse metabolic profiles than those whose conditions satisfied both IDF's definition and the new NCEP's definition.

DISCUSSION

The IDF definition would fail to identify a portion of people who have more than three MS component disorders but a small waistline. Further research and discussion are needed on whether and how to implement the IDF's definition.

Blood glucose measurements in the critically ill: more than just a blood draw

A crucial determinant for the success of intensive insulin therapy in critically ill patients is the frequent and accurate measurement of blood glucose values with immediate feedback of results. In general, therefore, this is achieved by point-of-care testing, raising the question of the best way of monitoring blood glucose. Corstjens and coworkers, in the previous issue of Critical Care, demonstrate that, in spite of good correlation to "conventional" laboratory glucose assessment, absolute glucose levels may differ systematically. This commentary reviews the problems of glucose measurements arising from matrix effects, interferences and the use of different assays.

Quality control of self-monitoring of blood glucose: why and how?

The control of analytical quality of self-monitoring of blood glucose (SMBG) is recommended as a routine procedure in diabetes management. This control procedure should be easily accessible to patients, convenient, not time-consuming, and provide a reliable assessment of glucose meter performance. Optimally it should be located in the diabetes outpatient clinic. Presently there are two approaches to carrying out SMBG quality control. The first is based on the comparison of results obtained by a controlled glucose meter and use of the laboratory method or point-of-care testing device as a surrogate reference analyzer. The second one is a traditionally organized external quality assessment scheme with use of a dedicated control material, which is distributed to all participants. The recommended allowable meter error in SMBG can be realistically set at 10%.

Urinary albumin excretion and its relation with C-reactive protein and the metabolic syndrome in the prediction of type 2 diabetes

OBJECTIVE

To investigate urinary albumin excretion (UAE) and its relation with C-reactive protein (CRP) and the metabolic syndrome in the prediction of the development of type 2 diabetes.

RESEARCH DESIGN AND METHODS

We used data from the Prevention of Renal and Vascular End Stage Disease (PREVEND) study, an ongoing, community-based, prospective cohort study initiated in 1997 in the Netherlands. The initial cohort consisted of 8,592 subjects. After 4 years, 6,894 subjects participated in a follow-up survey. Subjects with diabetes at baseline or missing data on fasting glucose were excluded, leaving 5,654 subjects for analysis. The development of type 2 diabetes, defined as a fasting glucose > or = 7.0 mmol/l and/or the use of antidiabetic medication, was used as the outcome measure. UAE was calculated as the mean UAE from two consecutive 24-h urine collections. Logistic regression models were used, with the development of type 2 diabetes as the dependent variable.

RESULTS

Of the 5,654 subjects for whom data were analyzed, 185 (3.3%) developed type 2 diabetes during a mean follow-up period of 4.2 years. UAE, CRP, and the presence of the metabolic syndrome at baseline were significantly associated with the incidence of type 2 diabetes (P < 0.001 for all variables). In a univariate model, the odds ratio (OR) for UAE was 1.59 (95% CI 1.42-1.79). In our full model, adjusted for age, sex, number of criteria of metabolic syndrome, and other known risk factors for the development of type 2 diabetes (including fasting insulin), the association between UAE and type 2 diabetes remained significant (OR 1.53, 95% CI 1.25-1.88, P < 0.001). There was a significant interaction between UAE and CRP (P = 0.002). After CRP was stratified into tertiles, the ORs for the association between baseline UAE and the development of type 2 diabetes were 2.2 (1.47-3.3), 1.33 (0.96-1.84), and 1.04 (0.83-1.31) for the lowest to highest tertiles, respectively.

CONCLUSIONS

UAE predicts type 2 diabetes independent of the metabolic syndrome and other known risk markers of development of type 2 diabetes. The predictive value of UAE was modified by the level of CRP.

Impact of statins in microalbuminuric subjects with the metabolic syndrome: a substudy of the PREVEND Intervention Trial

AIMS

Microalbuminuria frequently clusters with the metabolic syndrome and may identify subjects at increased coronary risk. Statin treatment may reduce the incidence of major adverse cardiac events in subjects with the metabolic syndrome, but evidence is limited. We evaluated the impact of pravastatin treatment on the incidence of major adverse cardiac events in microalbuminuric subjects with the metabolic syndrome.

METHODS AND RESULTS

This substudy of the PREVEND Intervention Trial (a randomized, placebo-controlled trial with a 2x2 factorial design) included 864 microalbuminuric subjects, who were randomized to fosinopril 20 mg or matching placebo and pravastatin 40 mg or matching placebo (mean follow-up 46 months). The metabolic syndrome was defined according to the NCEP ATPIII-report. Subjects with or without the metabolic syndrome were characterized by a higher age, male sex, and increased albuminuria. The incidence of major adverse cardiac events in subjects with the metabolic syndrome [9.1%; 95% confidence interval (CI) 6.0-13.0%] was increased vs. those without [3.6%; 95% CI 2.3-5.5%; P=0.007). Pravastatin treatment lowered the incidence of major adverse cardiac events in subjects with the metabolic syndrome after adjustment for age and sex (hazard ratio=0.39; 95% CI 0.17-0.89; P=0.025).

CONCLUSION

This study supports the use of statins in microalbuminuric subjects with the metabolic syndrome to reduce the incidence of major adverse cardiac events.

Laboratory tests in diagnosis and management of diabetes mellitus. Practical considerations

The clinical laboratory plays an essential role in both the diagnosis and therapeutic monitoring of diabetes mellitus. Here, a short review of the main laboratory parameters used in the monitoring of diabetes--glucose (including point-of-care testing), HbA1c and microalbuminuria--is presented. The impact of pre-analytical and analytical performance criteria on the clinical decision making process is discussed.

Evaluation of a glucose meter against analytical quality specifications for hospital use

BACKGROUND

The value of glucose meters in point of care testing (POCT) by medical professionals and self monitoring of blood glucose (SMBG) by patients is well established. We evaluated the SureSteppFlexx glucose meter against objective targets for imprecision and total error (TE).

METHODS

The SureStepFlexx blood glucose system uses a reflectance-based glucose oxidase (GO) method and reports plasma-equivalent glucose values. The reference method was the Beckman LX20 Pro glucose oxidase/oxygen electrode method. Patient samples and commercial aqueous, quality control (QC) material were used to assess imprecision. To determine total error of the meters, results obtained on patient heparinized blood were compared against results obtained by the reference method using plasma. Analyses were carried out by an experienced nurse and technologist.

RESULTS

Both operators achieved imprecision of < or =5% for all measurements. Overall, the percentage of results deviating from the total error targets were 0-21% and 4-13% for the nurse and technologist, respectively. Compared with earlier studies, the percentages of reported results outside the < or =10% and < or =5% American Diabetes Association (ADA) criteria are significantly lower.

CONCLUSIONS

The SureSteppFlexx glucose meter meets analytical quality requirements and is suitable for POCT use in our hospital. We propose a tiered approach and suggest minimum, desirable and optimum total error targets for glucose meters of < or =5%, 7.9% and 13%, respectively.

Problems of comparing blood glucose molality and molarity determined with an Omni, an EML 105 and an Ebio analyser

The comparability between glucose concentrations measured in various sample systems is still a matter of debate. Decision limits are usually determined in venous plasma and then converted to either blood or to the aqueous compartment (activity). The conversion factors recommended have not yet been generally accepted. In the present study, glucose concentrations were determined in blood and plasma with an Ebio analyser (molarity) and in the aqueous compartment with both an EML 105 and an Omni (molality). All analytical results were referred to the same aqueous standard solution. The Ebio results agreed with reference method values in control materials. Concentrations determined in the various sample systems from patients (molarity) correlated well with the molality values measured either with the EML or the Omni. However, the mean values of the EML were not consistent with those derived theoretically by considering the different water content. With the Omni, only molality values in whole blood appeared plausible, but not in plasma, although the two sample systems should provide identical molality values. The EML results were almost identical in whole blood and plasma. Theoretically, glucose molality would be the ideal diagnostic quantity. However, no diagnostic advantage of molality determined in whole blood with the Omni vs. molarity values could be detected in a group of 40 non-diabetic and 27 diabetic subjects.

Analytical performance of glucometers used for routine glucose self-monitoring of diabetic patients

BACKGROUND

Glucometry is an essential part of diabetes treatment, but so far, no standard quality control procedure verifying blood glucose meter results is available. In this study, we evaluated the analytical performance of eight glucose meters: GX and Esprit (Bayer Diagn.), MediSense Card Sensor, ExacTech (MediSense) with strips Selfcare (Cambridge Diagn), One Touch Basic, One Touch II, One Touch Profile (Lifescan) and Glucotrend (Boehringer Mannheim/Roche).

METHODS

The evaluation included within-run imprecision, linearity, comparison with the laboratory method and calculation of differences between individual glucometers.

RESULTS

Within-run imprecision ranged from 1.5% to 4.5%, linearity assessed as the correlation between measured and calculated glucose concentrations yielded r(2) values from 0.97 to 0.981. Analytical bias of glucose concentration values obtained by the glucometry amounted from 0.14% to 16.9% of values measured by the laboratory method. Bias higher than 5% was found for One Touch Basic, II and Profile meters (however, glucose concentrations in plasma obtained by the laboratory method One Touch meters showed analytical bias from 3.0% to 8.8%). The regression analysis yielded slope values from 0.77 to 1.09 and r(2) values from 0.86 to 0.98. The best correlations with the laboratory method were found for One Touch Basic, II Profile, Glucotrend and Esprit meters. The calculated differences between the individual glucose meters can constitute 0.02-1.49 mmol/l (0.96-26.9%) at glucose concentration 5.55 mmol/l, and 0.16-4.16 mmol/l (0.96-24.96%) at glucose concentration 16.67 mmol/l. Error grid analyses have shown that Glucometers One Touch Basic and One Touch Profile yielded all results in zone A (acceptable). The remaining glucometers yielded 1-7% of results in zones B (insignificant errors), C or D (lack of detection and treatment).

CONCLUSIONS

All studied glucometers had both small deviation from laboratory reference values (<10%) and high concurrence with results obtained by the laboratory method.

Evaluation of Portable Blood Glucose Meters. Problems and Recommendations

Various guidelines are in use for validating blood glucose meters. There are currently no analytical guidelines for validating procedures available for non-invasive or minimally invasive blood glucose meters. As a result manufacturers of these meters need to comply with different regulations in different countries with concomitant high costs. Some of the differences between different guidelines will be discussed in this article and recommendations for improvement will be presented.

Performance evaluation of blood glucose monitoring devices

Many new technologies are being applied to measure blood glucose concentrations, but there is a lack of a standardized approach to evaluate performance of these devices. We sought to identify the key elements in evaluating the performance of devices for measuring blood glucose. We examined these elements in a multicenter study of four brands of glucose meters that are commonly used by diabetic patients. We tested control materials, spiked whole blood specimens, and 461 heparinized whole blood specimens in triplicate by each of the four brand glucose meters, and analyzed the plasma glucose concentrations of these specimens by a hexokinase (HK) method that incorporated reference materials developed by National Institute of Standards and Technology. Testing with glucose meters was performed at three sites, with multiple operators, meters, and representative lots of reagents. We evaluated the systematic bias, random error, and clinical significance of glucose meters. Meters were precise with a coefficient of variation of <4% across a wide range of glucose concentrations. Slopes significantly different from 1.0 were observed for two meters with 11-13% and -11% to -13% at the 95% confidence interval level by the linear regression of meter results versus the HK method from 33 to 481 mg/dL (correlation coefficient >0.98 and standard error of estimation S(y/x) <13 mg/dL for both meters). Analysis of the clinical significance of bias by Clarke Error Grid showed that results of the four meters were outside the accurate zone (26.5%, 2.4%, 1.5%, and 5.6%). Only a small number of the results showed clinically significant bias, mostly in the hypoglycemic range. Meters performed consistently throughout the study and, generally, were precise, although precision varied at extremely high or low glucose concentrations. Two of the glucose meters had substantial systematic bias when compared with an HK method, indicating a need for improving calibration and standardization. Analytical performance varied over the physiological range of glucose values so that separate accuracy and precision goals should be defined for hypoglycemic, normoglycemic, and hyperglycemic ranges. This study describes the current state of performance of blood glucose monitoring devices and points out those factors that should be assessed during evaluation of new devices.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

Background: Multiple laboratory tests are used in the diagnosis and management of patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

Approach: An expert committee drafted evidence-based recommendations for the use of laboratory analysis in patients with diabetes. An external panel of experts reviewed a draft of the guidelines, which were modified in response to the reviewers’ suggestions. A revised draft was posted on the Internet and was presented at the AACC Annual Meeting in July, 2000. The recommendations were modified again in response to oral and written comments. The guidelines were reviewed by the Professional Practice Committee of the American Diabetes Association.

Content: Measurement of plasma glucose remains the sole diagnostic criterion for diabetes. Monitoring of glycemic control is performed by the patients, who measure their own plasma or blood glucose with meters, and by laboratory analysis of glycated hemoglobin. The potential roles of noninvasive glucose monitoring, genetic testing, autoantibodies, microalbumin, proinsulin, C-peptide, and other analytes are addressed.

Summary: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are of minimal clinical value at the present time, and measurement of them is not recommended.