Combining the power of advanced math with tests commonly used to measure blood sugar, scientists from Harvard Medical School (HMS) and Massachusetts General Hospital in the US have created a new model that more accurately accounts for long-term blood sugar fluctuations in people with diabetes.
The disease affects more than 422 million people worldwide, according to the World Health Organisation (WHO).
By factoring in the age of each patient's red blood cells, the new method offers a more precise, individualised gauge of three-month blood sugar averages and reduces in half the error rate of the most commonly used 'but sometimes inaccurate' test known as A1C, researchers said.
The A1C test led to notable off-target estimates in about a third of more than 200 patients whose test results were analysed as part of the research.
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The team found these inaccuracies stemmed entirely from individual variations in the life span of a person's red blood cells.
They calculated new, age-adjusted estimates and tested their predictive accuracy by comparing them to actual blood sugar levels measured directly via continuous glucose monitors wearable devices that read a person's blood sugar every five minutes.
Estimating a person's three-month blood sugar average is the best indicator of disease control and the most accurate predictor of looming complications, according to experts.
Persistently elevated blood sugar can, over time, damage the heart, brain, kidneys, eyes, nerves and other organs.
Since blood sugar varies by the hour and even by the minute, capturing "an average" to account for fluctuations over an extended period is a far better indicator of disease status than taking a "snapshot" measurement at one time.