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Can non-invasive features including limited joint mobility be used to predict glucose control?


Introduction Limited joint mobility (LJM) has been linked to deficient glycaemic control but is an understudied area of type 2 diabetes research. We set out to investigate the correlation between glycated haemoglobin (HbA1c) and the quantification of LJM of finger joints and non-invasive anthropometrics.

Methods Blood samples were taken from 170 participants at diabetes awareness drives in Trinidad. These participants were aged 59.61 ± 15.46, with a body mass index (BMI) of 29.73 ± 7.65 and HbA1c levels of 8.42 ± 2.22. There were 110 women and 60 men. Finger joint angles were recorded using a goniometer.

Results The K-Nearest Neighbour machine learning model was tested via 10-fold cross validation to differentiate good from poor glycaemic control (HbA1c ≤ 6.5%) using non-invasive features. There is some correlation between LJM and HbA1c. Our model scored a mean accuracy of 74.71% ± 1.81 (p=0.01) classifying the full dataset, 82.14% ± 2.20 (p=0.01) and 72.76% ± 1.41 (p=0.059) on the male/female subsets, respectively.

Discussion The time since diagnosis, age and BMI were important features linked to glucose control. Our results support the notion that the first signs of LJM in the fingers occur in the first and fifth fingers as these particular angles were ranked highly in the list of most important features.

Conclusion Our results show that LJM has some role to play in monitoring HbA1c, although not as important as more conventional anthropometrics. Our results support the idea that there should be a separate test for each sex.

  • diabetes
  • diagnostics
  • musculoskeletal

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