The predictive value of vascular calcification on plain radiographs of the foot to diagnose diabetes mellitus

T. Jennison, U. Naveed, C. Chadwick, C. Blundell

Introduction: There are nearly 500,000 people with undiagnosed diabetes mellitus in the UK. The incidental finding vascular calcification on plain radiographs in patients with undiagnosed diabetes has the potential to alter patient management in those presenting with pathology. We hypothesised that the presence of vascular calcification on plain radiographs of the foot may predict the diagnosis of diabetes. The primary aim of this case control study was to determine the positive predictive value of vascular calcification to diagnose diabetes. Secondary aims were to determine the odds of having diabetes dependent on other known risk factors for calcification.

Methods: A retrospective case control study of 130 diabetic patients were compared to 130 non-diabetic patients that were matched for age and gender. The presence of vascular calcification in anterior, posterior or plantar vessels, and length of calcification were measured on plain radiographs. McNemar’s Chi-squared test and positive predictive values were calculated. Conditional logistic regression models were used to estimate the association between calcification and diabetes.

Results: 28 patients had type I diabetes and 102 had type II diabetes. The mean age was 58.0 in both groups and 31.5% were females. 89.2% of those with diabetes had calcification present, and 23.1% in those without (p<0.0001). Calcification in two vessels predicts diabetes with a positive predictive value of 91.2% (95% CI 82.1%-100%). The odds ratio for having diabetes is 78 (95% CI: 7.8 – 784)  times higher in a person who has calcification in the blood vessels of their foot than in a person without calcification after adjusting for confounders.

Conclusion: This study has demonstrated that vascular calcification in 2 vessels is over 90% predictive of a diagnosis of diabetes. This screening test could be used in future clinics when interpreting radiographs, aiding in the diagnosis of diabetes and altering patient management.