A New Method to Assess Asymmetry in Fingerprints Could Be Used as an Early Indicator of Type 2 Diabetes Mellitus

J Diabetes Sci Technol. 2016 Jun 28;10(4):864-71. doi: 10.1177/1932296816629984. Print 2016 Jul.


Background: Inexpensive screening tools are needed to identify individuals predisposed to developing diabetes mellitus (DM). Such early identification coupled with an effective intervention could help many people avoid the substantial health costs of this disease. We investigated the hypothesis that fluctuating asymmetry (FA) in fingerprints is an indicator of type 2 diabetes mellitus (T2DM).

Methods: Participants with T2DM, with T1DM, and without any indication or known family history of diabetes were fingerprinted with a Crossmatch Verifier 320 LC scanner. Asymmetry scores for each finger pair were assessed using both pattern analysis (ridge counts), and a wavelet-based analysis.

Results: Both methods for scoring asymmetry predicted risk of T2DM for finger pair IV, controlling for gender and age. AUC scores were significantly greater than the null for pattern asymmetry scores (finger IV AUC = 0.74), and wavelet asymmetry scores for finger pair IV (AUC = 0.73) and finger pair V (AUC = 0.73), for predicting T2DM. In addition, wavelet asymmetry scores for finger pair IV (AUC = 0.80) and finger pair V (AUC = 0.85) significantly predicted risk of T1DM.

Conclusions: A diagnostic tool based on FA in the fingerprints of finger pair IV, measured using a wavelet analysis could be developed for predicting risk prior to associated health problems for both T2DM and T1DM. In addition, given that that the prints for fingers IV and V develop during the 14-17 weeks of gestation, we predict that interventions during this time period of pregnancy will be most successful.

Keywords: T1DM; T2DM; asymmetry; diagnostics; fingerprints; risk.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Dermatoglyphics*
  • Diabetes Mellitus, Type 1 / diagnosis
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Diabetes Mellitus, Type 2 / diagnostic imaging
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Male
  • Middle Aged