New
Model Predicts a Person’s Risk for Type 2 Diabetes
A
new simple mathematical model performs as well as the OGTT to
predict a person’s risk of developing type 2 diabetes and could
change diabetes care.
That’s what a
new study, by researchers at the University of Texas Health
Science Center, suggests. Along
with his colleagues, statistician Ken Williams collected data on
blood pressure, medical history and sugar levels after fasting and
during an OGT test for 1,791 Mexican Americans and 1,112 whites.
None had diabetes, and all were checked again 7.5 years later.
Williams then compared
the predictive accuracy of three models: one that included only
the OGT test results; one that used only the other clinical data;
and a third that combined both the clinical information and the
OGT test data.
For OGT data alone,
the predictive accuracy was 77.5 percent, while the clinical
data's predictive accuracy reached 84.3 percent. If both were used
together, the predictive accuracy peaked at 85.7 percent.
"Physicians can
do a better job of assessing risk for developing diabetes by
looking at the variety of indicators at their disposal from a
standard physical exam than they can by focusing entirely on the
results of an oral glucose tolerance test," Williams says.
Williams adds patients
might also prefer the mathematical model over the OGT test, which
requires that they fast for 12 hours, take a blood test, then wait
at their medical provider's office for another two hours for
another blood test. "That costs the patient two hours of
their time," Williams says.
Did
you know?
If
everyone who qualifies for screening under the latest standards
had OGT tests – including most minorities, non-Hispanic whites
over 45, and younger non-Hispanic whites with certain risk factors
– the indirect cost of lost work hours could be $1.16 billion to
$3.08 billion.
