PDF herunterladen
CC BY-NC-ND 4.0 · J Lab Physicians 2021; 13(01): 036-043
DOI: 10.1055/s-0041-1727557
Original Article

Comparative Assessment of ADA, IDRS, and FINDRISC in Predicting Prediabetes and Diabetes Mellitus in South Indian Population

Parveen Doddamani
1   Department of Biochemistry, JSS Medical College and Hospital, JSS Academy of Higher Education and Research, Bannimantap, Mysuru, Karnataka, India
,
Nitin Ramanathan
1   Department of Biochemistry, JSS Medical College and Hospital, JSS Academy of Higher Education and Research, Bannimantap, Mysuru, Karnataka, India
,
N. K. Swetha
1   Department of Biochemistry, JSS Medical College and Hospital, JSS Academy of Higher Education and Research, Bannimantap, Mysuru, Karnataka, India
,
M. N. Suma
1   Department of Biochemistry, JSS Medical College and Hospital, JSS Academy of Higher Education and Research, Bannimantap, Mysuru, Karnataka, India
› Institutsangaben
› Weitere Informationen
   Lizenzen und Reprints

Abstract

Introduction Diabetes risk-screening tools are validated and implemented across various countries. There is a need for improvement in these risk scores with suitable modifications so as to make them more sensitive, specific, and suitable to the local population.

Objectives The aim of this study was to evaluate and compare the diagnostic accuracy and clinical utility of the Indian diabetes risk score (IDRS), the American diabetic association (ADA) risk score, and the Finnish Diabetes Risk Score in healthy subjects of South Indian origin in predicting the risk of diabetes and to correlate these risk scores with the blood glucose and hemoglobin A1c (HbA1c) levels in the study population.

Materials and Methods A total of 160 subjects attending the master health checkup/outpatient department of a tertiary care hospital were included in the study. Each subject was asked to fill a questionnaire. Details obtained using the questionnaire were assessed as per the three diabetic risk scores. Fasting blood sugar/random blood sugar and HbA1c were estimated.

Statistical Analysis Used Data analysis was done using SPSS 22/23. Pearson correlation was used to compare continuous variables, with p < 0.05 considered statistically significant. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell’s clinical utility indices were calculated for each risk tool.

Results We found the prevalence of diabetes to be 11.9%. ADA risk score was the only risk score that showed a statistically significant difference (p-value = 0.05) between the low- and high-risk subjects.

Conclusions ADA or IDRS risk scores can be used for screening diabetes in the South Indian population. We suggest that inclusion of the history of gestational diabetes and hypertension in the IDRS risk score might improve its sensitivity as a screening tool in our local population.

Keywords

ADA risk score - IDRS - prediabetes - diabetes risk scores - FINDRISC


Publikationsverlauf

Artikel online veröffentlicht:
24. Mai 2021

© 2021. The Indian Association of Laboratory Physicians. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

 

  • References

  • 1 Dudeja P, Singh G, Gadekar T, Mukherji S. Performance of Indian Diabetes Risk Score (IDRS) as screening tool for diabetes in an urban slum. Med J Armed Forces India 2017; 73 (02) 123-128
    CrossrefPubMedGoogle Scholar
  • 2 International Diabetes Federation. IDF Diabetes Atlas, 9th edition. Brussels, Belgium: International Diabetes Federation; 2019. http://www.diabetesatlas.org. Accessed September 25, 2020
  • 3 Nagalingam S, Sundaramoorthy K, Arumugan B. Screening for diabetes using Indian Diabetes Risk Score. Indian J Adv Med 2016; 3 (02) 415-418
    Google Scholar
  • 4 Omech B, Mwita JC, Tshikuka J-G, Tsima B, Nkomazna O, Amone-P’Olak K. Validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. J Diabetes Res 2016; 2016: 4968350
    CrossrefPubMedGoogle Scholar
  • 5 Woo YC, Lee CH, Fong CHY, Tso AWK, Cheung BMY, Lam KSL. Validation of the diabetes screening tools proposed by the American Diabetes Association in an aging Chinese population. PLoS One 2017; 12 (09) e0184840
    CrossrefPubMedGoogle Scholar
  • 6 Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes 2018; 12 (06) 517-525
    CrossrefPubMedGoogle Scholar
  • 7 Joshi SR. Indian Diabetes Risk Score. J Assoc Physicians India 2005; 53: 755-757
    PubMedGoogle Scholar
  • 8 Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Assoc Physicians India 2005; 53: 759-763
    PubMedGoogle Scholar
  • 9 American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care 2020; 43 (Suppl. 01) S14-S31
    CrossrefPubMedGoogle Scholar
  • 10 Aziz N, Kallur SD, Nirmalan PK. Implications of the revised consensus body mass indices for Asian Indians on clinical obstetric practice. J Clin Diagn Res 2014; 8 (05) OC01-OC03
    PubMedGoogle Scholar
  • 11 Bays HE, Chapman RH, Grandy S. SHIELD Investigators’ Group. The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys [published correction appears in Int J. Clin Pract 2007; 61 (10) 1777-1778
    Google Scholar
  • 12 Bae JP, Lage MJ, Mo D, Nelson DR, Hoogwerf BJ. Obesity and glycemic control in patients with diabetes mellitus: analysis of physician electronic health records in the US from 2009-2011. J Diabetes Complications 2016; 30 (02) 212-220
    CrossrefPubMedGoogle Scholar
  • 13 Pawar SD, Naik JD, Prabhu P, Jatti GM, Jadhav SB, Radhe BK. Comparative evaluation of Indian Diabetes Risk Score and Finnish Diabetes Risk Score for predicting risk of diabetes mellitus type II: a teaching hospital-based survey in Maharashtra. J Family Med Prim Care 2017; 6 (01) 120-125
    CrossrefPubMedGoogle Scholar
  • 14 Adhikari P, Pathak R, Kotian S. Validation of the MDRF-Indian Diabetes Risk Score (IDRS) in another South Indian population through the Boloor Diabetes Study (BDS).. J Assoc Physicians India 2010; 58: 434-436
    PubMedGoogle Scholar
  • 15 Sowmiya KR, Balaji SM, Arumugam B, Mohanan S. Indian Diabetic Risk Score-a screening tool for detecting type 2 diabetes mellitus at the primary health care level. Natl J Res Community Med 2017; 6 (01) 69-72
    Google Scholar
  • 16 Vandersmissen GJM, Godderis L. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) for diabetes screening in occupational health care. Int J Occup Med Environ Health 2015; 28 (03) 587-591
    CrossrefPubMedGoogle Scholar
  • 17 Chaturvedi M, Pandey A, Javed M, Baiswar R. Validity of Indian Diabetes Risk Score (IDRS) in population in and around Agra. J Assoc Physicians India 2018; 66 (10) 33-35
    PubMedGoogle Scholar
  • 18 Sharma KM, Ranjani H, Nguyen H. et al. Indian Diabetes Risk Score helps to distinguish type 2 from non-type 2 diabetes mellitus (GDRC-3).. J Diabetes Sci Technol 2011; 5 (02) 419-425
    CrossrefPubMedGoogle Scholar
  • 19 Prabhu G, Poovitha M, Jayasri S. To determine the usefulness of ADA risk score to predict T2dm/prediabetes in South Indian rural population. Int J Contemporary Med Res 2019; 6 (08) H27-H30
    Google Scholar