Analytical Interferences and
Physiological Limitations of
Blood Glucose Meters
Ken Ervin
Published information
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Package inserts
Review articles (partial list)
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Boren and Clarke
Tonyushkina and Nichols
Pitkin and Rice
Montagnana et al
Wahl
Dungan
Arabadjief and Nichols
Heller and Feldman
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Specific articles (partial list)
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Kimberly, et al
Fiore and Delanghe
Lyon, et al
Kazmierczak and Catrou
Goudable, et al
Zheng, et al
Vesper, et al
Katelijne and Delanghe
Tang, et al
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Package inserts address “Procedural
limitations”
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Sample related
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e.g. Hct, pO2, DKA, HHNK, etc.
Endogenous compounds
Exogenous compounds
Environmental
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Temperature
Humidity
Altitude
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The limitations of a product are
dependent upon the choice of
technology to achieve the design goals.
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BGM Design Goals
Drive the specifications and choice of
technology
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Accurate and precise
Highly specific
*Stable at room
temperature
*Rapid test (use whole
blood directly)
*Very easy to use
Small blood volume
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*Inexpensive meter
*Cal code strategy
Low cost/test
More recently
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No pO2 dependence
No maltose interference
No hematocrit effect
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To meet the specifications,
technologies are chosen for the
measurement device and its method
of production
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BGM measurement based on
combining technologies
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Method of introducing sample to device
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Method to identify glucose in sample (specificity)
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Enzymatic reaction (GO, GDH, Hexokinase/G6PDH)
Method to quantify glucose
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Most devices now rely on capillary action, sometimes in
two directions
Colorimetric
Electrochemical
Method of calibration
Methods to assess performance of the test or
correct results
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Interferences and physiological
limitations are related to choices of
sample type and technology
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Interferences result from
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Analyte specificity issues or
Sample and environmental influences on the
measurement reaction
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Analyte specificity
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Use of enzymes specific for glucose
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GO
GDH
Hexokinase/G6PDH
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Sample influences on measurement
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Endogenous substances
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Uric acid
Bilirubin
Lipemia, Hemolysis
Exogenous substances
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Acetominophen
Ascorbate
Maltose, Icodextrin metabolites
Mannitol
Dopamine
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Sample influences
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DKA, HHNK
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pH and/or Viscosity
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Hyperosmolar, flow effects
Less water volume to reconstitute reagent
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Environmental influences
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Analytical Variability
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Temperature
Humidity
Altitude (i.e. oxygen availability)
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Physiological limitations
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Sample choice
 Capillary, venous, or arterial
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Actual concentrations are different and relationship may vary
If capillary; hypotension, perfusion and other conditions such
as Reynaud’s syndrome disturb normal relationship
Alternate site time lag
pO2 differences
Hematocrit
Smaller sample sizes increase the potential for
residue to influence results
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Some relevant examples
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How a pO2 dependence became a maltose
interference
Hematocrit effects
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The pO2 effect
GO
glucose + O2 + H2O
H2O2 + dye precursor
glucose + med (ox)
med (red)
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GO
Epot
gluconic acid + H2O2
HRPO
(YSI and Beckman
Glucose Analyzer)
dye color + H20
gluconolactone + med (red)
e- + med (ox)
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(colorimetric)
(electrochemical)
How a pO2 interference became a
maltose interference
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Original methods based on glucose oxidase coupled
to a colorimetric indicator system.
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Oxygen available from atmosphere
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Electrochemical methods used mediators
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blood removed by blotting, wiping etc.
exposed to air during the reaction time
Systems calibrated for capillary blood
Oxygen would interfere competitively
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Use of venous or arterial blood exacerbated this competition
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Venous reads higher; less 02 competition
Arterial reads lower; more 02 competition
pO2 effects generally greater at lower glucose concentrations
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How a pO2 interference became a
maltose interference
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Second Generation products
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GO
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Open to atmospheric oxygen
Oxygen blocked by windows or capillary design
Hexokinase/G6PDH
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How a pO2 interference became a
maltose interference
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GDH-PQQ systems introduced to alleviate pO2
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However, GDH-PQQ less specific for glucose
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GDH reaction does not involve oxygen
RT stable enzyme
Recognizes maltose, galactose, xylose and other sugars
with glucose moiety, with false elevation of glucose results.
Recent versions of GDH with NAD or FAD cofactor
are more specific and stable.
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Hematocrit effects
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For a rapid test, WB is preferable if not necessary
Most systems now report “plasma equivalent”
Systems are calibrated at normal hematocrit.
WB sample hematocrits may vary significantly
(~15 to >70)
Glucose content of whole blood as compared to
plasma is inversely related with hematocrit.
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Hematocrit dependence
Hematocrit effect
90
8040
7030
Bias to plasma (%)
6020
5010
40 0
30
-10
Physiological effect
0
20
40
60
80
Little method effect
Greater method effect
20
-20
10
-30
0
-40
1st Qtr
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2nd Qtr
Hematocrit
3rd Qtr
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4th Qtr
Hematocrit effects
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Hematocrit may influence access of plasma or
diffusion of glucose to measurement system
suppressing results.
Hematocrit effects generally greater at higher
glucose concentrations
Hematocrit can be measured and corrected for
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Greater imprecision?
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In Conclusion
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Limitations and interferences are related to the
particular technologies chosen.
The unique goals of a BGM system make it unlikely
they will ever completely match a lab based system.
The evolution of BGM devices is a demonstration of
achieving a balance between a high degree of
performance with a rapid, more versatile, easy to
use system.
Using a WB sample and reporting plasma (unless
corrected for) introduces a ± 6% error in the range
25-65 hct.
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