Diabetes: New Diagnostic Tools Research Paper Example

The research was aimed at finding the differences in fingerprints patterns between people with type II diabetes and control group of people without diabetes and defining whether the dermatoglyphics analysis can be used as a tool for prediction and early diagnostic tool for diabetes mellitus type II. The research population included 350 people (240 men and 110 women) aged 25-60 and a control group with 350 participants.
Dermatoglyphics studies the patterns of hands and fingertips. The early methods of fingerprints classification were developed in the alte 19th century, and the three key patterns were identified (whorl, loop and arch, Henry, as cited in Nilesh & Balbhim, 2011.) The today’s classification uses more diverse classification of patterns and subpatterns. The analysed study compared the fingertip patterns of the patients with type II diabetes mellitus with a random non-diabetic group, with excusion of “co-morbid patients with associated cardiac, renal and other life threatening diseases and hand deformity patients (Nilesh & Balbhim, 2011.)
In the group of diabetic patients, the researchers noticed the increased incidence of whorl on both hands; this type of fingertip pattern was typical for both men and women with type II diabetes. According to the study results, in diabetic men on the right hand there’s 632 whorls in average, while in the control there was only 446 whorls, that is 42% less than in the studied group (Nilesh & Balbhim, 2011.) This difference is significant at 0.000 level (P-value). The similar difference is noticed for left hand in males, and also for both hands in female groups. In contrast, the control group demonstrated the greater incidence of other patterns such as arches and loops.
In general, the study findings emphasise an increased number of whorl, lesser number of loops and arch in type II diabetes patients, as compared with non-diabetic control groups. The results are in line with the similar studies, for example, with the studies of Kahn and colleagues (2009), evidencing, that the fingerprints analysis is the valid tool for predicting diabetes. But Kahn and colleagues study went even deeper in their analysis. According to their research, fetal programming of diabetes type II, manifestating in adult age, originates in early gestation, when fingerprints are established. As a result, dermatoglyphic analysis appeared to be a useful tool “to investigate prenatal developmental plasticity” (Kahn et al., 2009) and to predict diabetes type II using a fingerprint marker.
The different studies on dermatoglyphics defined different patterns and models accosiated with increased risk of diabetes. In the study by Nilesh & Balbhim (2014) the scholars analysed the number of types and subtypes of loops, arches and whorls and found out that diabetes is associated with the increased number of whorls. In contrast, in the study by Dr. Karim J. Karim and A.L. Mohammed A. Saleem (2014,) analyzing smaller population of diabetes II patients (50 people), the researchers identified that “plane arches in diabetic males and ulnar loops, radial loops and tented arches in diabetic females were increased significantly” as compared to the control groups, while whorls demonstrated decsreased incidence.
In the study by Kahn et al., the authors used the Md15 marker, a variable, formed basing on fingertip's ridge count, and state that the patients with higher values of Md15 were more likely to have diabetes in their middle age (2009.) But all of the above mentioned studies have their limitations. Nevertheless, they provide a valuable input in understanding the mechanisms of fetal plasticity.
The dermatoglyphic studies of fingerprints provide a significant tool in early prediction and assessment of predisposition to diabetes II type. Additional studies are required in the domain of analyzing hand patterns, association of fingertip patterns with age of diabetes manifestation, the influence of environmental factors. After the significant body of research is accumulated, there’ll be a possibility to develop methods and protocols enabling high probability of predicting the risk of diabetes using dermatoglyphic tools and methods. If these diagnostic methods will be widely used in medical practice, there’ll be also possible to introduce instant diabetes risk assessment tools applying progressive technologies (digital and mobile).

References

Nilesh, R., & Balbhim, Z. (2014). Fingertip patterns: A diagnostic tool to predict diabetes mellitus. National Journal of Medical and Dental Research, 2(3), 46-50. Retrieved from http://search.proquest.com/docview/1556081365?accountid=12779
Schwarz, P.E., Brunswick, Ph. & Calvet, J.-H. (2011). EZSCAN™ A New Technology to Detect Diabetes Risk. British Journal of Diabetes and Vascular Disease. 2011;11(4):204-209. Retrieved from http://www.medscape.com/viewarticle/750234
Karim, J.K. & Saleem, A.L.M. (2014). Dermatoglyphics Study of Finger Prints Pattern’s Variations of a Group of Type II Diabetic Mellitus Patients in Erbil City. Zanco Journal of Pure and Applied Sciences. Vol.26, No.4, 2014. Retrieved from http://zancojournals.su.edu.iq/index.php/JPAS/article/view/83/75
Kahn, H. S., Graff, M., Stein, A. D., & Lumey, L. H. (2009). A fingerprint marker from early gestation associated with diabetes in middle age: The Dutch Hunger Winter Families Study. International Journal of Epidemiology, 38(1), 101–109. doi:10.1093/ije/dyn158 Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2639363/