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Assessment of risk of diabetes by using Indian Diabetic risk score (IDRS) in Indian population
diabetes research and clinical practice
162 (2020) 108088
Contents available at ScienceDirect
Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locat e/dia bre s
Assessment of risk of diabetes by using Indian Diabetic risk score (IDRS) in Indian population Raghuram Nagarathna b,*, Rahul Tyagi a, Priya Battu a, Amit Singh b, Akshay Anand a,1, Hongasandra Ramarao Nagendra b a b
Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India Swami Vivekananda Yoga Research Foundation, Bengaluru, India
A R T I C L E I N F O
A B S T R A C T
Article history:
Aims: To screen the Indian population for Type 2 Diabetes Mellitus (DM) based on Indian
Received 7 December 2019
Diabetes Risk Score. Our main question was; Does Indian Diabetic risk score (IDRS) effec-
Received in revised form
tively screen diabetic subjects in Indian population?
30 January 2020
Methods: Multi-centric nationwide screening for DM and its risk in all populous states and
Accepted 18 February 2020
Union territories of India in 2017. It is the first pan India DM screening study conducted on
Available online 19 February 2020
240,000 subjects in a short period of 3 months based on IDRS. This was a stratified translational research study in randomly selected cluster populations from all zones of rural and
Keywords: T2DM Diabetic Yoga Protocol IDRS Diagnosis DYP
urban India. Two non-modifiable (age, family history) and two modifiable (waist circumference & physical activity) were used to obtain the score. High, moderate and low risk groups were selected based on scores. Results: In this study 40.9% subjects were detected to be high risk, known or newly diagnosed DM subjects in urban and rural regions. IDRS could detect 78.1% known diabetic subjects as high risk group. Age group 50–59 (17.4%); 60–69 (22%); 70–79 (22.8%); >80 (19.2%) revealed high percentage of subjects. ROC was found to be 0.763 at CI 95% of 0.761–0.765 with statistical significance of p < 0.0001. At >50 cut off, youden index showed the sensitivity of 78.05 and specificity of 62.68. Regression analysis revealed that IDRS and Diabetes are significantly positively associated. Conclusions: Data reveals that IDRS is a good indicator of high risk diabetic subjects. Ó 2020 Elsevier B.V. All rights reserved.
1.
Introduction:
As per the International Diabetes Federation (IDF), the number of people with Type 2 Diabetes (DM) is increasing in each country. Currently, 387 million people are living with Diabetes across the world, and it is expected to rise to a whopping figure of 592 million in 2035 [1]. In the year 2000, India had high-
est number of DM patients followed by china and US. This DM patient population is expected to increase to 79.4 million by 2030 in India [36]. Co-morbidities associated with Diabetes and resulting mortality go unidentified because of late diagnosis and delay in initiation of therapy. This is largely preventable by early diagnosis of DM and increasing awareness about the disease both in public as well as among the
* Corresponding author at: Swami Vivekananda Yoga Research Foundation (SVYASA), Bengaluru, India. E-mail addresses: [email protected] (R. Nagarathna), [email protected] (A. Anand). 1 Co-Corresponding author at: Neuroscience Research Lab, Department of Neurology, PGIMER, Chandigarh, India. https://doi.org/10.1016/j.diabres.2020.108088 0168-8227/Ó 2020 Elsevier B.V. All rights reserved.
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diabetes research and clinical practice
health-care providers. The strategy for the prevention of DM is mainly based on the regulation of modifiable risk factors. Bassuk and Manson have reviewed studies where 30 min/ day moderate physical activity was linked with reduced risk of development of DM and cardiovascular diseases. They concluded that physical exercise helps in weight reduction, regulation of blood pressure, inflammation, improvement in insulin sensitivity etc. showing that changes in modifiable risk factors helps in lower risk of DM development. The population may, therefore, be divided into two target groups-high risk individuals and the remaining population. A strong argument exists in favor of screening for participants who are at an increased risk for DM [2]. Attempts have been made to devise risk scores to screen population for DM [3–6]. The Indian Diabetes Risk Score (IDRS), has been emerged as a simple screening tool for prediction of undiagnosed DM, which was developed by Mohan et al. at the Madras Diabetes Research Foundation (MDRF), Chennai. The score referred to as MDRF-IDRS was derived from the Chennai Urban Rural Epidemiology Population Study (CURES) and was internally validated using the data from the Chennai Urban Population Study [7]. Several other studies also have demonstrated the sensitivity, specificity and accuracy of MDRFIDRS [8]. The IDRS is a cost effective simple tool based on four simple parameters derived from known risk factors for DM, two modifiable risk factors (waist circumference and physical inactivity) and two non-modifiable risk factors (age and family history of diabetes) which may be amenable to intervention [9]. Significant correlation between BMI and IDRS, with DM in rural area of Tamil Nadu has been reported. It was found that with increase in BMI likelihood of maximum diabetic score was also high [10]. Additionally, Mohan et al. has also estimated the cost-effectiveness of MDRF-IDRS for population screening and found it to be of low cost and user friendly for screening Prediabetes and DM in population [11]. IDRS has been shown to be having a sensitivity of 95.12% and specificity of 28.95% in DM subjects with >60 score[12]. A north Indian study has shown its 100% sensitivity at a cut off value 30 recommending it to screen medium to high risk DM cases [13]. Studies have also reported an excellent predictive capacity of IDRS to undiagnosed DM conditions [8,14]. However, no nationwide study with large sample size has been carried out to estimate its utility for global application. The study data will benefit the government to frame as well as implement national diabetic control programmes in different zones, aged populations, genders and social settings. Subject Recruitment and Screening: The objective of the first phase of this study was to conduct a multi-centric nationwide screening for DM and its risk in all states/union territories of India in 2017. Subjects were recruited based on defined inclusion and exclusion criteria under the guidelines of Institutional ethics Committee (Vide Res/IEC-IYA/001 dated 16.12.16). This community based study was called Niyantrita Madhumeha Bharata Abhiyaan (NMB-2017)(Diabetes control mission in India) and was carried out by Indian Yoga association. It was funded by the Ministry of Health and Family Welfare and the Ministry of AYUSH, Govt. of India, New Delhi. Methodological details were used according to previous study
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[15]. Briefly, the steps included: a) Formation of international research advisory committee of 16 experts who designed the study protocol and monitor the quality control processes adopted at various levels of the rapid survey. b) Random selection of districts (1/10) from all states/Union territories, followed by random selection of clusters of urban (cities/towns) and rural (villages) areas across India. c) Screening of all men and women above 20 years of age in all households in these selected areas covering a population of 4000/ District (50% rural and 50% urban) by 1200 trained field volunteers (20/ district), further monitored by 35 senior research fellows and 2 research associates. Door to door screening was carried out by requesting information in the screening form (hard copy and mobile app) that consisted of questions related to age, gender, education, occupation, marital status, socio-economic status (education of Head, occupation of head, family income), Diabetes information, IDRS risk factors, body vitals (height, weight, hip circumference) and diet information.
1.1.
Risk assessment tool
Indian Diabetes Risk Score (IDRS), developed and validated by Madras Diabetes Research Foundation (MDRF), Chennai [7], was administered to detect high risk population. IDRS comprises of two modifiable (waist circumference, physical activity) and two non-modfiable risk factors (age, family history) for Diabetes.
1.2.
Procedure
1.2.1.
Subjects
Subjects were recruited based on defined inclusion and exclusion criteria as per the guidelines of Institution Ethics Committee. The screening of all men and women above 20 years of age covering a population of 4000/District (50% rural and 50% urban) was carried out. Door to door screening was carried out by requesting information in the screening form (hard copy and mobile app) that consisted of questions related to age, gender, education, occupation, marital status, socio-economic status (education of Head, occupation of Head, family income), Diabetes information (history of diabetes, time since diagnosis, whether undergoing any treatment or not etc), IDRS risk factors, body vitals (height, weight, hip circumference) and diet information. Information related to IDRS (age, physical activity at home/work, family history) was collected from all those above 20 years of age in all households in the selected area. The trained field research volunteers who visited each household recorded the information about age, physical activity and family history during the interview, in a hard copy of the screening form. The waist circumference (in centimeters) was measured using a flexible (non metallic) 6 m long measuring tape that had centimeter/millimeter marking. The individuals were asked to remove their clothing around the abdomen, stand straight with both feet together, raise the upper limbs, and stay relaxed. The measuring tape was wrapped around the abdomen between iliac crest and the lower margin of the rib cage, and the umbilicus in front. Measurement was taken
60 = High Risk 30–50 = Moderate risk IDRS Risk Score
Physical activity at home or work Modifiable Risk Factor
Male: Waist circumference: Female: Waist circumference:
<35 years = 10 Both non-diabetic parents = 0 Age Family history: Non-modifiable Risk Factor
<30 = Low Risk
90–99 cm = 20 80–89 cm = 20
100 cm = 30 90 cm = 30
no exercise = 30 Mild exercise = 20 Moderate exercise = 10
35–49 years = 20 One parent having Diabetes = 10 Scores
Majority of known DM subjects (78.1%) were found in the high risk group confirming the importance of IDRS. The north western region remains the highest prevalence zone followed by South India (in the urban region) whereas north Indian rural areas showed high risk diabetic subjects. Out of 766 low risk candidates, only 0.3% were from J&K region whereas central Indian urban as well as rural regions were found to report more cases in this category. Statistical analysis has been shown in the Table 4. Table 4 Age wise distribution of IDRS: Data indicates that 50–59 age groups are crucial in the screening of DM subjects. A shift of 10% increase in the high risk category was observed in this age group. The increase in age, as anticipated, had an increased proportion of high risk individuals, 50–59 (17.4%);
Parameters
Distribution of known DM subjects
Table 1 – IDRS Scoring based on non-modifiable and modifiable risk factors.
Results
Demographic Details: Pan-India, a total of 240,000 individuals were recruited for screening based on IDRS. Demographical details including age, sex, Body-mass Index, Waist circumference, socio-economic Status have been tabulated in Table 2. Zone wise prevalence: Zone wise prevalence was estimated 40.9% prevalence of high risk, whether known or newly diagnosed DM subjects, in urban (23.1%) and rural (17.8%) regions. Moreover, 29.7% populations were found to have moderate risk for DM. Regions including North, North West (J&K), North East (NE), Central, West, East and South, were segregated. Among these Zones, urban settings of J&K region showed 33% prevalence with (0.321–0.337) of 95% CI, however, rural areas of North Indian Zone showed 27.5% prevalence. South Indian urban Zone showed an increased number of moderate risk group (23.3%) whereas North Indian rural Zone showed 19.5% prevalence. Table 3 depicts the prevalence in various urban and rural zones. Table 3 shows the zone wise distribution of IDRS risk factors. The highest percentage of high risk population (33. %) was in northwest zone (Jammu and Kashmir). Least percentage of high risk population (15.7%) was in the East zone. Subjects in the urban areas were at higher risk than the rural area.
2.1.
50 years = 30. Both Diabetic parents = 20
by keeping the tape parallel to the ground. Special care was taken to ensure that there are no twists in the tape and it does not cause any compression in the skin.). Scoring for 4 different risk factors of IDRS was done as per Table 1. Only those individuals with high score of 60 on IDRS were called for the second level of testing meant for blood tests and detailed data. Statistical analysis: The statistical analysis was carried out using SPSS software in order to analyze the mean, standard deviations, proportions and CI at 95%. P value <0.05 was considered statistically significant. ROC curve was plotted to analyze the sensitivity and specificity of IDRS among various risk groups. Chi square test was used to test the significance between proportions. Entire statistical analysis was conducted at SVYASA, Bangalore and raw data deposited there.
2.
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162 (2020) 108088
Vigorous exercise or strenuous work = 0 less than < 90 cm = 0 <80 cm = 0
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Table 2 – Demographic details of the screened population in different zones of India. Zone
area
Age Mean (SD) Total Rural Urban
Central (n = 24854) East (n = 22430) J&K (n = 14495) North (n = 17602) North East (n = 16251) South (n = 39338) West (n = 27360)
41.1 (13.69) 41.8 (13.8)
40.5 (13.3) 39.06 (12.94) 42.15 (13.65)
40.4 (13.7) 41.01 (13.87) 40 (13.59)
41.8 (13.5) 41.52 (13.44) 42.06 (13.7)
41.0 (13.5) 40.42 (13.24) 42.25 (13.89)
41.2 (13.3) 40.41 (12.98) 42.18 (13.62)
41 (13.9) 42.73 (14.71) 40.01 (13.34)
43.4 (14.3) 42.67 (14.4) 44.16 (14.31)
13,255 (53.3) (51.3%) 6122 (46.2%) (48.7%) 7133 (43.8%) 1:1.16 11,573 (46.6) (52.1%) 5936 (51.3%) (47.9%) 5637 (48.7%) 1:0.95
11,169 (49.8) 5550 (49.6%) 5619 (50.4%) 1:1.01 11,248 (50.1) 5929 (52.7%) 5319 (47.3%) 1:089
6891 (47.5) 4159 (60.4%) 2732 (39.6%) 1:0.66 7587 (52.3) 4201 (50.2%) 3386 (49.8%) 1:0.81
7602 (43.2) 2909 (38.3%) 4693 (61.7%) 1:1.6 9987 (56.7) 3364 (33.7%) 6623 (66.3%) 1:1.96
7439 (45.8) 3600 (48.4%) 3839 (51.6%) 1:1.17 8804 (54.2) 4222 (48%) 4582 (52%) 1:1.1
17,506 (44.5) 10,566 (60.4%) 6940 (39.6%) 1:0.66 21,264 (55.5) 13,213 (62.1%) 8051 (37.8%) 1:0.61
140,663 6500 (50.2%) 7132 (49.8%) 1:1.11 14,393 (52.6) 7382 (51.3%) 7011 (48.7%) 1:0.95
Sex
Male Urban Rural Ratio Female Urban Rural Ratio
BMI*
Overall 24.6 (4.7)
19.09 (3.69)
18.5 (3.23)
19.7 (3.29)
19.5 (3.96)
19.0 (3.35)
19.8 (3.27)
19.7 (3.67)
WC*
Male 88.8 (11.3) Female 85.20 (14.3) Overall 86.8 (13.3)
89.5 (12.4) 83.8 (11.8) 86.9 (12.4)
85.6 (8.9) 81.8 (9.7) 83.7 (9.5)
91.6 (8.7) 89.4 (11.6) 90.4 (9.8)
89 (11.6) 87.9 (13.1) 88.3 (12.5)
87.6 (9.8) 85.4 (10.9) 86.4 (10.4)
88.4 (13.9) 83.1 (20.3) 85.3 (18.2)
90.2 (11.0) 87.1 (12.0) 88.5 (11.6)
SES*
Low 51,664 (41.6) Middle 52,222 (42.1) High 17,007 (13.4)
7583 (54.7) 4703 (33.9) 1554 (11.2)
8909 (57.4) 5632 (36.3) 1962 (6.2)
4424 (32.2) 8088 (58.9) 2209 (8.8)
6595 (47.8) 5647 (40.9) 2540 (11.1)
6681 (44.5) 7064 (47.1) 1236 (8.25)
10,392 (38.3) 11,900 (43.9) 4800 (17.7)
7080 (37.3) 9188 (48.4) 2706 (14.2)
76,801 39,407 37,394 1:0.94 84,856 44,247 40,609 1:0.92
Table 3 – Distribution of IDRS scores in urban and rural areas in different zones. Area
J&K (NW) n (%) 95%CI
NE n (%) 95%CI
North n (%) 95%CI
Central n (%) 95%CI
West n (%) 95%CI
East n (%) 95%CI
South n (%) 95%CI
Total n (%) 95%CI
High risk (>60)
Urban
4771 (33.0%) (0.321–0.337) 2886 (19.9%) (0.192–0.205)
3413 (21%) (0.203–0.216) 2746 (16.9%) (0.146–0.158)
3372 (19.2%) (0.186–0.197) 4823 (27.5%) (0.267–0.281)
4160(16.8%) (0.162–0.172) 1749(7.0%) (0.067–0.073)
7544(27.7%) (0.271–0.281) 6088(22.0%) (0.218–0.228)
3513(15.7%) (0.152–0.161) 3234 (14.4%) (0.139–0.148)
8311(29.0%) (0.284–0.295) 5567 (19.4%) (0.189–0.198)
35.084(23.1%) (0.229–0.233) 27,073(17.8%) (0.176–0.180)
2033 (14.0%) (0.134–0.146) 1861 (12.9%) (0.123–0.134)
2349 (14.5%) (0.139–0.150) 2689(16.6%) (0.159–0.171)
1677(9.5%) (0.091–0.099) 3433(19.5%) (0.189–0.201)
3049 (12.3% (0.118–0.127) 3020(12.2%) (0.117–0.125)
3493 (12.8%) (0.124–0.132) 3770(13.8%) (0.134–0.142)
3896 (17.4%) (0.168–0.178) 4117 (18.4%) (0.178–0.188)
6670 (23.3%) (0.227–0.237) 3114 (10.9%) (0.104–0.112)
23,167(15.2%) (0.151–0.154) 22,004(14.5%) (0.143–0.146)
Urban
1556 (10.7%) (0.102–0.112)
2057 (12.7%) (0.121–0.131)
1207(6.9%) (0.065–0.072)
4843(19.5%) (0.190–0.200)
2804(10.3%) (0.099–0.106)
4065(18.1%) (0.176–0.186)
2667(9.3%) (0.089–0.096)
19,199(12.6%) (0.125–0.128)
Rural
1371 (9.5%) (0.089–0.099)
2985 (18.4%) (0.177–0.189)
3055 (17.4%) (0.168–0.179)
7996 (32.2%) (0.316–0.328)
3657 (13.4%) (0.130–0.138)
3583 (16.0%) (0.155–0.164)
2358 (8.2%) (0.079–0.085)
25,005(16.5%) (0.163–0.166)
14,478
16,238
17,567
24,817
27,276
22,408
28,687
1,51,532
Rural Moderate risk(30–50)
Urban Rural
Low risk (<30)
Total
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IDRS
diabetes research and clinical practice
All India
Area
J&K (NW) n (%) 95%CI
NE n (%) 95%CI
North n (%) 95%CI
Central n (%) 95%CI
West n (%) 95% CI
East n (%) 95% CI
South n (%) 95%CI
=Total n (%) 95%CI
High risk (>60)
Urban
634 (62.9) (0.598–0.659) 273 (27.1) (0.243–0.299) 907 (8.01%)
592 (45.7) (0.430–0.485) 295 (22.8) (0.205–0.232) 887 (7.8%)
469 (32.5) (0.300–0.349) 723 (50.1) (0.474–0.527) 1192(10.53%)
722 (49.7) (0.471–0.523) 266 (18.3) (0.163–0.204) 988 (8.73%)
1490 (48.0) (0.462–0.497) 1094 (35.2) (0.335–0.369) 2584(22.84%)
669 (37.1) (0.365–0.411) 553 (30.7) (0.285–0.329) 1,222 (10.80)
2401 (55.0) (0.535–0.565) 1101 (25.2) (0.239–0.265) 3502 (30.95)
7007 (48.3) (0.47–0.49) 4305 (29.7%) (0.29–0.30) 11,312
55 (5.4) (0.04–0.07) 29 (2.87) (0.019–0.041) 84
202 (15.6) (0.136–0.177) 121 (9.35) (0.078–0.110) 323
91 (6.3) (0.05–0.07) 103 (7.1) (0.058–0.085) 194
162 (11.1) (0.095–0.128) 99 (6.8) (0.055–0.082) 261
257 (8.2) (0.073–0.083) 170 (5.4) (0.047–0.063) 427
209 (11.6) (0.101–0.131) 228 (12.6) (0.111–0.142) 437
431 (9.9) (0.09–0.108) 253 (5.8) (0.051–0.065) 684
1407 (9.7%) (0.092–0.102) 1003 (6.9%) (0.065–0.073) 2410
4 (0.39) (0.001–0.01) 12 (1.1) (0.006–0.02) 16
42 (3.2) (0.023–0.043) 41 (3.17) (0.022–0.042) 83
16 (1.1) (0.006–0.017) 41 (2.8) (0.02–0.038) 57
81 (5.5) (0.044–0.068) 122 (8.4) (0.070–0.099) 203
54 (1.7) (0.013–0.022) 39 (1.25) (0.008–0.017) 93
83 (4.6) (0.036–0.056) 58 (3.2) (0.024–0.041) 141
104 (2.38) (0.019–0.028) 69 (1.58) (0.012–0.019) 173
384 (2.69%) (0.023–0.029) 382 (2.62%) (0.023–0.029) 766
1007
1293
1443
1452
3104
1800
4359
14,458
Rural Total Moderate risk(30–50)
Urban Rural Total
Low risk (<30)
Urban Rural Total
Zone wise Total
Note: >95% of self reported diabetics were in high and moderate scores on IDRS.
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IDRS
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Table 4 – Distribution of known Diabetes patients in different ranges of risk scores on IDRS in Rural and Urban sectors of different zones of India.
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162 (2020) 108088
Table 5 – Age wise distribution of IDRS in known diabetes patients in the Screened population. Age range
IDRS High
Moderate
Low
Total number
Known DM
Total number
Known DM
Total number
Known DM
<20 20–29 30–39 40–49 50–59 60–69 70–79 >80
196 1810 9045 15,558 16,617 11,191 2408 262
18 (0.5%) 112 (0.4%) 643 (1.9%) 2336 (7.6%) 3959 (17.4%) 3355 (22%) 737 (22.8%) 75 (19.8%)
793 8421 12,806 10,149 4824 3310 672 91
12(0.3%) 108 (0.4%) 363 (1.1%) 778 (2.5%) 566 (2.5%) 438 (2.9%) 110 (3.4%) 14 (3.7%)
2940 16,811 12,343 4858 1296 731 152 25
26 (0.7%) 161 (0.6%) 199 (0.6%) 185 (0.6%) 93 (0.4%) 67 (0.4%) 11 (0.3%) 3 (0.8%)
Total
57,087
11,215
41,066
2376
39,156
745
Table 6 – Sensitivity and Specificity of IDRS. Criterion
Sensitivity
95% CI
Specificity
95% CI
+LR
95% CI
0 >0 >10 >20 >30 >40 >50 >60 >70 >80 >90 >100
100.00 99.54 99.11 97.99 94.70 88.66 78.05 61.32 40.74 17.64 4.11 0.00
100.0–100.0 99.4–99.6 98.9–99.3 97.7–98.2 94.3–95.1 88.1–89.2 77.4–78.7 60.5–62.1 39.9–41.5 17.0–18.3 3.8–4.4 0.0–0.03
0.00 3.14 8.81 18.24 31.24 45.13 62.68 76.50 88.27 96.45 99.49 100.00
0.0–0.003 3.0–3.2 8.7–9.0 18.0–18.5 31.0–31.5 44.9–45.4 62.4–62.9 76.3–76.7 88.1–88.5 96.3–96.6 99.5–99.5 100.0–100.0
1.00 1.03 1.09 1.20 1.38 1.62 2.09 2.61 3.47 4.97 8.12
1.0–1.0 1.0–1.0 1.1–1.1 1.2–1.2 1.4–1.4 1.6–1.6 2.1–2.1 2.6–2.7 3.4–3.6 4.7–5.2 7.3–9.1
LR
95% CI
0.15 0.10 0.11 0.17 0.25 0.35 0.51 0.67 0.85 0.96 1.00
0.1–0.2 0.09–0.1 0.10–0.1 0.2–0.2 0.2–0.3 0.3–0.4 0.5–0.5 0.7–0.7 0.8–0.9 1.0–1.0 1.0–1.0
Table 7 – a & b: Regression analysis showing prediction of self reported diabetes by IDRS. a) Multinomial regression Parameter Estimates IDRS3riskfcatora
1.00
Intercept [PreRdiabetes [PreRdiabetes Intercept [PreRdiabetes [PreRdiabetes
2.00
B
= 0.0] = 1.0] = 0.0] = 1.0]
Std. Error
2.597 0.988 0b 1.100 0.410 0b
0.158 0.165 . 0.083 0.090 .
Wald
269.822 35.747 . 174.425 20.787 .
df
Sig.
1 1 0 1 1 0
Odds ratio
<0.001 <0.001 . <0.001 <0.001 .
95% CI for Exp (B) Lower Bound
Upper Bound
2.686 .
1.943 .
3.713 .
1.507 .
1.264 .
1.798 .
a. The reference category is: 3.00. b. This parameter is set to zero because it is redundant. b) Binary logistic regression Variables in the Equation B
a
Step 1
IDRS3riskfcator IDRS3riskfcator(1) IDRS3riskfcator(2) Constant
0.578 0.988 2.489
S.E.
0.177 0.165 0.159
a. Variable(s) entered on step 1: IDRS3riskfcator.
Wald
49.774 10.708 35.747 245.927
df
2 1 1 1
Sig.
<0.001 0.001 <0.001 <0.001
Odds ratio
1.782 2.686 0.083
95% C.I.for EXP (B) Lower
Upper
1.261 1.943
2.519 3.713
diabetes research and clinical practice
60–69 (22%); 70–79 (22.8%); >80 (19.2%). Age wise distribution has been shown in Table 5
2.2.
Sensitivity & specificity of IDRS
ROC curve was plotted for 137,947 participants. Area under the ROC curve was found to be 0.763 at CI 95% of 0.761 to 0.765 with statistical significance of p < 0.0001. Youden index at >50 criterion, the sensitivity of 78.05 and specificity of 62.68 was observed. Sensitivity and specificity at different criterion have been provided in Table 6. Prediction of self reported diabetes through IDRS was found to be positively significantly associated with odds ratios 1.782 (1.261–2.519) and 2.686 (1.943–3.713) as provided in Table 7.
3.
Discussion
IDRS is one of the cost-effective methods to detect the DM risk among the Indian population.[8,16] This is the first nationwide study on 240,000 population conducted within 3 months in all zones of India. Based on IDRS data, we report that 40.9% & 29.7% of known DM subjects fall in high risk and moderate risk groups, respectively. This increased to 78.1% among the known subjects. However, North western J&K and south Indian zones were found to be affected. In a Lucknow based study, conducted on 272 subjects, 67.2% were found to be high risk. [17] Similar studies reported high risk populations of 43% [18] and 19% in the rural Tamil nadu [10]. Undoubtedly, IDRS has emerged as a sensitive tool to detect undiagnosed Diabetic subjects, though the sensitivity and specificity scores varied in various studies. Dudega et al reported a sensitivity of 95.12% and specificity of 28.95% at the cutoff score of >60. [12] Similarly, Adhikari et al reported best sensitivity (62.2%) and specificity of (73.7%) at the cut off IDRS score of 60[8]. However, a large study based on 26,000 subjects identified IDRS detection sensitivity and specificity to be 72.5% and 60.1% respectively (for determining undiagnosed diabetes). [7]. Diabetes prediction scales have been developed in various other populations [19,20]. Different diabetes risk scores including FINDRISC [21], DANISH[22], DESIR[23], ARIC[24] and QDScore[25] have also been used for predicting diabetes in different populations. We observed that females are at a higher risk for development of DM with the highest number of cases in North Indian females [26] as compared to males. Previous studies, based on different zones, have reported mixed results where some researchers indicated gender differences [27,28] while others found it more prevalent in females [29] or males [30–32]. Geographical distribution of DM showed a maximum number of Diabetic subjects in west and south zone and the zone with the lowest number of Diabetic incidence was Central followed by Eastern zone. A study done by Agarwal and Ebrahim seeking to screen the variations in Diabetes prevalence in different geographical regions in India, reported maximum incidence of Diabetic subjects in south Indian states like Kerala and Goa in comparison with the central zone state like Rajasthan [31]. The MDRF–IDRS is considered a simple tool of assessment, since a non-physician may collect the data based on age,
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family history, physical activity and a single measurement of waist circumference. Moreover, its accuracy strengthens the utility for screening Diabetic subjects[33] especially in India where more than 41 million are suffering with Diabetes while majority among these are unaware of it. IDRS is thus a good screening tool before carrying out the blood sugar test in the population. Further, the risk assessment using IDRS score has revealed that more than half of Indian population (55.7%) falls under high risk of developing DM. Therefore, a big increase in the Diabetes subjects in India in near future is expected. This may be partly due to higher proportion of population falling in the middle and old age group. Similar rising trends of DM in India have been reported by other studies [34]. Other studies have similarly used IDRS for screening high risk population and found about 41% [7] and 31.5% [35] population to fall under this category. Females were reported to have higher risk of DM incidence than males. Besides, age was found to be a strong risk factor for its occurrence [28]. It was found that DM incidence is expected to increase in South, North and West Zones in near future. Ominously, DM is spreading very rapidly in Indian population which requires immediate preventive public health initiatives like multiple educational and awareness programs in the direction for prevention and amelioration of DM. Conclusion: IDRS score distribution showed higher prevalence of DM patients falling into high risk group. >50 cut off youden index showed the sensitivity of 78.05 and specificity of 62.68 which approves the utility of IDRS as a cost effective tool. IDRS was found to be a strong predictor for cases with diabetes. IDRS tool based on this study findings have public health implications. Moreover method can be utilized by the practicing clinicians in early diagnosis of DM.
Funding This research was funded by Central Council for Research in Yoga and Naturopathy (CCRYN), New Delhi (Ref F.No. 16-63/2 016-17/CCRYN/RES/Y&D/MCT/Dated: 15.12.2016).
Declaration of Competing Interest None exists.
Acknowledgement We acknowledge Ministry of AYUSH, Govt of India, New Delhi, for funding this project. We also acknowledge support of CCRYN for manpower, MOHFW for supporting the cost of investigations and IYA for the overall project implementation. We thank the advisory research committee, senior research fellows, Mr Sabzar, Dr Sanjay, Ms Radhika, Dr Sunanda Rathi, Yoga volunteers and the President of Indian Yoga Association for their contribution in this project.
Author contributions R.N. and H.R.N. Conceptualization, Data Curation and acquisition, Funding Acquisition, Supervision R.N. and A.S. Formal Analysis, Investigation, Methodology, Validation and Writ-
8
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ing—review and editing. R.T. and P.B. Drafting of the Original draft, critical review and editing. A.A. Concept of manuscript.
Appendix A. Supplementary material
[16]
Supplementary data to this article can be found online at https://doi.org/10.1016/j.diabres.2020.108088. [17] R E F E R E N C E S
[18] [1] IDF I. Diabetes Atlas Update Poster. IDF; 2014. [2] Harrison TA, Hindorff LA, Kim H, Wines RC, Bowen DJ, McGrath BB, et al. Family history of diabetes as a potential public health tool. Am J Prev Med 2003;24:152–9. [3] Kaushal K, Mahajan A, Parashar A, Dhadwal DS, Jaswal V, Jaret P, et al. Validity of madras diabetes research foundation: Indian diabetes risk score for screening of diabetes mellitus among adult population of urban field practice area, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India. Ind J Endocrinol Metabol 2017;21:876. [4] Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 2012;345 e5900. [5] Long J, Rozo-Rivera A, Akers T, VanGeest JB, Bairan A, Fogarty KJ, et al. Validating the utility of the Spanish version of the American Diabetes Association Risk Test. Clin Nurs Res 2006;15:107–18. [6] Al-Lawati J, Tuomilehto J. Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East. Diabetes Res Clin Pract 2007;77:438–44. [7] Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Associat Phys India 2005;53:759–63. [8] 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;58:434–6. [9] Joshi SR. Indian diabetes risk score. JAPI 2005;53:755–7. [10] Gupta SK, Singh Z, Purty AJ, Kar M, Vedapriya D, Mahajan P, et al. Diabetes prevalence and its risk factors in rural area of Tamil Nadu. Ind J Commun Med: Offi Publ Ind Associat Prevent Soc Med 2010;35:396. [11] Mohan V, Anbalagan VP. Expanding role of the Madras Diabetes Research Foundation - Indian Diabetes Risk Score in clinical practice. Indian J Endocrinol Metab 2013;17:31–6. [12] 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;73:123–8. [13] Kaushal K, Mahajan A, Parashar A, Dhadwal DS, Jaswal VMS, Jaret P, et al. Validity of Madras Diabetes Research Foundation: Indian Diabetes Risk Score for Screening of Diabetes Mellitus among Adult Population of Urban Field Practice Area, Indira Gandhi Medical College, Shimla, Himachal Pradesh. India Indian J Endocrinol Metab 2017;21:876–81. [14] Bhadoria AS, Kasar PK, Toppo NA. Validation of Indian diabetic risk score in diagnosing type 2 diabetes mellitus against high fasting blood sugar levels among adult population of central India. Biomed J 2015;38:359–60. [15] Nagarathna R, Rajesh SK, Amit S, Patil S, Anand A, Nagendra HR. Methodology of Niyantrita Madhumeha Bharata
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Abhiyaan-2017, a Nationwide Multicentric Trial on the Effect of a Validated Culturally Acceptable Lifestyle Intervention for Primary Prevention of Diabetes: Part 2. Int J Yoga 2019;12:193–205. Nagarathna R, Tyagi R, Kaur G, Vendan V, Acharya IN, Anand A, et al. Efficacy of a Validated Yoga Protocol on Dyslipidemia in Diabetes Patients: NMB-2017 India Trial. Medicines (Basel) 2019;6. Khan MM, Sonkar GK, Alam R, Mehrotra S, Khan MS, Kumar A, et al. Validity of Indian Diabetes Risk Score and its association with body mass index and glycosylated hemoglobin for screening of diabetes in and around areas of Lucknow. J Family Med Prim Care 2017;6:366–73. Mohan V, Sandeep S, Deepa R, Shah B, Varghese C. Epidemiology of type 2 diabetes: Indian scenario. Indian J Med Res 2007;125:217–30. Salinero-Fort MA, de Burgos-Lunar C, Mostaza Prieto J, Lahoz Rallo C, Abanades-Herranz JC, Gomez-Campelo P, et al. Validating prediction scales of type 2 diabetes mellitus in Spain: the SPREDIA-2 population-based prospective cohort study protocol. BMJ Open 2015;5 e007195. Rathmann W, Martin S, Haastert B, Icks A, Holle R, Lowel H, et al. Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med 2005;165:436–41. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003;26:725–31. Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T, Borch-Johnsen K, et al. A Danish diabetes risk score for targeted screening: the Inter99 study. Diabetes Care 2004;27:727–33. Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care 2008;31:2056–61. Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, et al. Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care 2005;28:2013–8. Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338 b880. Misra A, Pandey RM, Devi JR, Sharma R, Vikram NK, Khanna N. High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in northern India. Int J Obes Relat Metab Disord 2001;25:1722–9. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094–101. Bharati DR, Pal R, Kar S, Rekha R, Yamuna TV, Basu M. Prevalence and determinants of diabetes mellitus in Puducherry. South India J Pharm Bioallied Sci 2011;3:513–8. Dasappa H, Fathima FN, Prabhakar R, Sarin S. Prevalence of diabetes and pre-diabetes and assessments of their risk factors in urban slums of Bangalore. J Family Med Prim Care 2015;4:399–404. Zargar AH, Khan AK, Masoodi SR, Laway BA, Wani AI, Bashir MI, et al. Prevalence of type 2 diabetes mellitus and impaired glucose tolerance in the Kashmir Valley of the Indian subcontinent. Diabetes Res Clin Pract 2000;47:135–46. Agrawal S, Ebrahim S. Prevalence and risk factors for selfreported diabetes among adult men and women in India: findings from a national cross-sectional survey. Public Health Nutr 2012;15:1065–77.
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[32] Gupta OP, Joshi MH, Dave SK. Prevalence of diabetes in India. Adv Metab Disord 1978;9:147–65. [33] Gupta RKST, Verma AK, Raina SK. Utility of MDRF-IDRS (Madras Diabetes Research Foundation-Indian Diabetes Risk Score) as a tool to assess risk for diabetes-a study from north-west India. Int J Diabetes Devel Countries 2015;35:570–2. [34] Ramachandran A. Epidemiology of diabetes in India–three decades of research. J Assoc Physicians India 2005;53:34–8.
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[35] Chowdhury R. MAaKLS, A study on distribution and determinantsof indian diabetic risk score (idrs) among rural population of west bengal ISSN: 2249 4995|eISSN: 2277 8810. A study on distribution and determinantsof indian diabetic risk score (idrs) among rural population of west bengal. Natl J Med Res 2012;2:282–6. [36] Kaveeshwar SA, Cornwall J. The current state of diabetes mellitus in India. Austral Med J 2014;7(1):45–8. https://doi.org/ 10.4066/AMJ.2013.1979.
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Contents available at ScienceDirect
Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locat e/dia bre s
Assessment of risk of diabetes by using Indian Diabetic risk score (IDRS) in Indian population Raghuram Nagarathna b,*, Rahul Tyagi a, Priya Battu a, Amit Singh b, Akshay Anand a,1, Hongasandra Ramarao Nagendra b a b
Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India Swami Vivekananda Yoga Research Foundation, Bengaluru, India
A R T I C L E I N F O
A B S T R A C T
Article history:
Aims: To screen the Indian population for Type 2 Diabetes Mellitus (DM) based on Indian
Received 7 December 2019
Diabetes Risk Score. Our main question was; Does Indian Diabetic risk score (IDRS) effec-
Received in revised form
tively screen diabetic subjects in Indian population?
30 January 2020
Methods: Multi-centric nationwide screening for DM and its risk in all populous states and
Accepted 18 February 2020
Union territories of India in 2017. It is the first pan India DM screening study conducted on
Available online 19 February 2020
240,000 subjects in a short period of 3 months based on IDRS. This was a stratified translational research study in randomly selected cluster populations from all zones of rural and
Keywords: T2DM Diabetic Yoga Protocol IDRS Diagnosis DYP
urban India. Two non-modifiable (age, family history) and two modifiable (waist circumference & physical activity) were used to obtain the score. High, moderate and low risk groups were selected based on scores. Results: In this study 40.9% subjects were detected to be high risk, known or newly diagnosed DM subjects in urban and rural regions. IDRS could detect 78.1% known diabetic subjects as high risk group. Age group 50–59 (17.4%); 60–69 (22%); 70–79 (22.8%); >80 (19.2%) revealed high percentage of subjects. ROC was found to be 0.763 at CI 95% of 0.761–0.765 with statistical significance of p < 0.0001. At >50 cut off, youden index showed the sensitivity of 78.05 and specificity of 62.68. Regression analysis revealed that IDRS and Diabetes are significantly positively associated. Conclusions: Data reveals that IDRS is a good indicator of high risk diabetic subjects. Ó 2020 Elsevier B.V. All rights reserved.
1.
Introduction:
As per the International Diabetes Federation (IDF), the number of people with Type 2 Diabetes (DM) is increasing in each country. Currently, 387 million people are living with Diabetes across the world, and it is expected to rise to a whopping figure of 592 million in 2035 [1]. In the year 2000, India had high-
est number of DM patients followed by china and US. This DM patient population is expected to increase to 79.4 million by 2030 in India [36]. Co-morbidities associated with Diabetes and resulting mortality go unidentified because of late diagnosis and delay in initiation of therapy. This is largely preventable by early diagnosis of DM and increasing awareness about the disease both in public as well as among the
* Corresponding author at: Swami Vivekananda Yoga Research Foundation (SVYASA), Bengaluru, India. E-mail addresses: [email protected] (R. Nagarathna), [email protected] (A. Anand). 1 Co-Corresponding author at: Neuroscience Research Lab, Department of Neurology, PGIMER, Chandigarh, India. https://doi.org/10.1016/j.diabres.2020.108088 0168-8227/Ó 2020 Elsevier B.V. All rights reserved.
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health-care providers. The strategy for the prevention of DM is mainly based on the regulation of modifiable risk factors. Bassuk and Manson have reviewed studies where 30 min/ day moderate physical activity was linked with reduced risk of development of DM and cardiovascular diseases. They concluded that physical exercise helps in weight reduction, regulation of blood pressure, inflammation, improvement in insulin sensitivity etc. showing that changes in modifiable risk factors helps in lower risk of DM development. The population may, therefore, be divided into two target groups-high risk individuals and the remaining population. A strong argument exists in favor of screening for participants who are at an increased risk for DM [2]. Attempts have been made to devise risk scores to screen population for DM [3–6]. The Indian Diabetes Risk Score (IDRS), has been emerged as a simple screening tool for prediction of undiagnosed DM, which was developed by Mohan et al. at the Madras Diabetes Research Foundation (MDRF), Chennai. The score referred to as MDRF-IDRS was derived from the Chennai Urban Rural Epidemiology Population Study (CURES) and was internally validated using the data from the Chennai Urban Population Study [7]. Several other studies also have demonstrated the sensitivity, specificity and accuracy of MDRFIDRS [8]. The IDRS is a cost effective simple tool based on four simple parameters derived from known risk factors for DM, two modifiable risk factors (waist circumference and physical inactivity) and two non-modifiable risk factors (age and family history of diabetes) which may be amenable to intervention [9]. Significant correlation between BMI and IDRS, with DM in rural area of Tamil Nadu has been reported. It was found that with increase in BMI likelihood of maximum diabetic score was also high [10]. Additionally, Mohan et al. has also estimated the cost-effectiveness of MDRF-IDRS for population screening and found it to be of low cost and user friendly for screening Prediabetes and DM in population [11]. IDRS has been shown to be having a sensitivity of 95.12% and specificity of 28.95% in DM subjects with >60 score[12]. A north Indian study has shown its 100% sensitivity at a cut off value 30 recommending it to screen medium to high risk DM cases [13]. Studies have also reported an excellent predictive capacity of IDRS to undiagnosed DM conditions [8,14]. However, no nationwide study with large sample size has been carried out to estimate its utility for global application. The study data will benefit the government to frame as well as implement national diabetic control programmes in different zones, aged populations, genders and social settings. Subject Recruitment and Screening: The objective of the first phase of this study was to conduct a multi-centric nationwide screening for DM and its risk in all states/union territories of India in 2017. Subjects were recruited based on defined inclusion and exclusion criteria under the guidelines of Institutional ethics Committee (Vide Res/IEC-IYA/001 dated 16.12.16). This community based study was called Niyantrita Madhumeha Bharata Abhiyaan (NMB-2017)(Diabetes control mission in India) and was carried out by Indian Yoga association. It was funded by the Ministry of Health and Family Welfare and the Ministry of AYUSH, Govt. of India, New Delhi. Methodological details were used according to previous study
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[15]. Briefly, the steps included: a) Formation of international research advisory committee of 16 experts who designed the study protocol and monitor the quality control processes adopted at various levels of the rapid survey. b) Random selection of districts (1/10) from all states/Union territories, followed by random selection of clusters of urban (cities/towns) and rural (villages) areas across India. c) Screening of all men and women above 20 years of age in all households in these selected areas covering a population of 4000/ District (50% rural and 50% urban) by 1200 trained field volunteers (20/ district), further monitored by 35 senior research fellows and 2 research associates. Door to door screening was carried out by requesting information in the screening form (hard copy and mobile app) that consisted of questions related to age, gender, education, occupation, marital status, socio-economic status (education of Head, occupation of head, family income), Diabetes information, IDRS risk factors, body vitals (height, weight, hip circumference) and diet information.
1.1.
Risk assessment tool
Indian Diabetes Risk Score (IDRS), developed and validated by Madras Diabetes Research Foundation (MDRF), Chennai [7], was administered to detect high risk population. IDRS comprises of two modifiable (waist circumference, physical activity) and two non-modfiable risk factors (age, family history) for Diabetes.
1.2.
Procedure
1.2.1.
Subjects
Subjects were recruited based on defined inclusion and exclusion criteria as per the guidelines of Institution Ethics Committee. The screening of all men and women above 20 years of age covering a population of 4000/District (50% rural and 50% urban) was carried out. Door to door screening was carried out by requesting information in the screening form (hard copy and mobile app) that consisted of questions related to age, gender, education, occupation, marital status, socio-economic status (education of Head, occupation of Head, family income), Diabetes information (history of diabetes, time since diagnosis, whether undergoing any treatment or not etc), IDRS risk factors, body vitals (height, weight, hip circumference) and diet information. Information related to IDRS (age, physical activity at home/work, family history) was collected from all those above 20 years of age in all households in the selected area. The trained field research volunteers who visited each household recorded the information about age, physical activity and family history during the interview, in a hard copy of the screening form. The waist circumference (in centimeters) was measured using a flexible (non metallic) 6 m long measuring tape that had centimeter/millimeter marking. The individuals were asked to remove their clothing around the abdomen, stand straight with both feet together, raise the upper limbs, and stay relaxed. The measuring tape was wrapped around the abdomen between iliac crest and the lower margin of the rib cage, and the umbilicus in front. Measurement was taken
60 = High Risk 30–50 = Moderate risk IDRS Risk Score
Physical activity at home or work Modifiable Risk Factor
Male: Waist circumference: Female: Waist circumference:
<35 years = 10 Both non-diabetic parents = 0 Age Family history: Non-modifiable Risk Factor
<30 = Low Risk
90–99 cm = 20 80–89 cm = 20
100 cm = 30 90 cm = 30
no exercise = 30 Mild exercise = 20 Moderate exercise = 10
35–49 years = 20 One parent having Diabetes = 10 Scores
Majority of known DM subjects (78.1%) were found in the high risk group confirming the importance of IDRS. The north western region remains the highest prevalence zone followed by South India (in the urban region) whereas north Indian rural areas showed high risk diabetic subjects. Out of 766 low risk candidates, only 0.3% were from J&K region whereas central Indian urban as well as rural regions were found to report more cases in this category. Statistical analysis has been shown in the Table 4. Table 4 Age wise distribution of IDRS: Data indicates that 50–59 age groups are crucial in the screening of DM subjects. A shift of 10% increase in the high risk category was observed in this age group. The increase in age, as anticipated, had an increased proportion of high risk individuals, 50–59 (17.4%);
Parameters
Distribution of known DM subjects
Table 1 – IDRS Scoring based on non-modifiable and modifiable risk factors.
Results
Demographic Details: Pan-India, a total of 240,000 individuals were recruited for screening based on IDRS. Demographical details including age, sex, Body-mass Index, Waist circumference, socio-economic Status have been tabulated in Table 2. Zone wise prevalence: Zone wise prevalence was estimated 40.9% prevalence of high risk, whether known or newly diagnosed DM subjects, in urban (23.1%) and rural (17.8%) regions. Moreover, 29.7% populations were found to have moderate risk for DM. Regions including North, North West (J&K), North East (NE), Central, West, East and South, were segregated. Among these Zones, urban settings of J&K region showed 33% prevalence with (0.321–0.337) of 95% CI, however, rural areas of North Indian Zone showed 27.5% prevalence. South Indian urban Zone showed an increased number of moderate risk group (23.3%) whereas North Indian rural Zone showed 19.5% prevalence. Table 3 depicts the prevalence in various urban and rural zones. Table 3 shows the zone wise distribution of IDRS risk factors. The highest percentage of high risk population (33. %) was in northwest zone (Jammu and Kashmir). Least percentage of high risk population (15.7%) was in the East zone. Subjects in the urban areas were at higher risk than the rural area.
2.1.
50 years = 30. Both Diabetic parents = 20
by keeping the tape parallel to the ground. Special care was taken to ensure that there are no twists in the tape and it does not cause any compression in the skin.). Scoring for 4 different risk factors of IDRS was done as per Table 1. Only those individuals with high score of 60 on IDRS were called for the second level of testing meant for blood tests and detailed data. Statistical analysis: The statistical analysis was carried out using SPSS software in order to analyze the mean, standard deviations, proportions and CI at 95%. P value <0.05 was considered statistically significant. ROC curve was plotted to analyze the sensitivity and specificity of IDRS among various risk groups. Chi square test was used to test the significance between proportions. Entire statistical analysis was conducted at SVYASA, Bangalore and raw data deposited there.
2.
3
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Vigorous exercise or strenuous work = 0 less than < 90 cm = 0 <80 cm = 0
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4
Table 2 – Demographic details of the screened population in different zones of India. Zone
area
Age Mean (SD) Total Rural Urban
Central (n = 24854) East (n = 22430) J&K (n = 14495) North (n = 17602) North East (n = 16251) South (n = 39338) West (n = 27360)
41.1 (13.69) 41.8 (13.8)
40.5 (13.3) 39.06 (12.94) 42.15 (13.65)
40.4 (13.7) 41.01 (13.87) 40 (13.59)
41.8 (13.5) 41.52 (13.44) 42.06 (13.7)
41.0 (13.5) 40.42 (13.24) 42.25 (13.89)
41.2 (13.3) 40.41 (12.98) 42.18 (13.62)
41 (13.9) 42.73 (14.71) 40.01 (13.34)
43.4 (14.3) 42.67 (14.4) 44.16 (14.31)
13,255 (53.3) (51.3%) 6122 (46.2%) (48.7%) 7133 (43.8%) 1:1.16 11,573 (46.6) (52.1%) 5936 (51.3%) (47.9%) 5637 (48.7%) 1:0.95
11,169 (49.8) 5550 (49.6%) 5619 (50.4%) 1:1.01 11,248 (50.1) 5929 (52.7%) 5319 (47.3%) 1:089
6891 (47.5) 4159 (60.4%) 2732 (39.6%) 1:0.66 7587 (52.3) 4201 (50.2%) 3386 (49.8%) 1:0.81
7602 (43.2) 2909 (38.3%) 4693 (61.7%) 1:1.6 9987 (56.7) 3364 (33.7%) 6623 (66.3%) 1:1.96
7439 (45.8) 3600 (48.4%) 3839 (51.6%) 1:1.17 8804 (54.2) 4222 (48%) 4582 (52%) 1:1.1
17,506 (44.5) 10,566 (60.4%) 6940 (39.6%) 1:0.66 21,264 (55.5) 13,213 (62.1%) 8051 (37.8%) 1:0.61
140,663 6500 (50.2%) 7132 (49.8%) 1:1.11 14,393 (52.6) 7382 (51.3%) 7011 (48.7%) 1:0.95
Sex
Male Urban Rural Ratio Female Urban Rural Ratio
BMI*
Overall 24.6 (4.7)
19.09 (3.69)
18.5 (3.23)
19.7 (3.29)
19.5 (3.96)
19.0 (3.35)
19.8 (3.27)
19.7 (3.67)
WC*
Male 88.8 (11.3) Female 85.20 (14.3) Overall 86.8 (13.3)
89.5 (12.4) 83.8 (11.8) 86.9 (12.4)
85.6 (8.9) 81.8 (9.7) 83.7 (9.5)
91.6 (8.7) 89.4 (11.6) 90.4 (9.8)
89 (11.6) 87.9 (13.1) 88.3 (12.5)
87.6 (9.8) 85.4 (10.9) 86.4 (10.4)
88.4 (13.9) 83.1 (20.3) 85.3 (18.2)
90.2 (11.0) 87.1 (12.0) 88.5 (11.6)
SES*
Low 51,664 (41.6) Middle 52,222 (42.1) High 17,007 (13.4)
7583 (54.7) 4703 (33.9) 1554 (11.2)
8909 (57.4) 5632 (36.3) 1962 (6.2)
4424 (32.2) 8088 (58.9) 2209 (8.8)
6595 (47.8) 5647 (40.9) 2540 (11.1)
6681 (44.5) 7064 (47.1) 1236 (8.25)
10,392 (38.3) 11,900 (43.9) 4800 (17.7)
7080 (37.3) 9188 (48.4) 2706 (14.2)
76,801 39,407 37,394 1:0.94 84,856 44,247 40,609 1:0.92
Table 3 – Distribution of IDRS scores in urban and rural areas in different zones. Area
J&K (NW) n (%) 95%CI
NE n (%) 95%CI
North n (%) 95%CI
Central n (%) 95%CI
West n (%) 95%CI
East n (%) 95%CI
South n (%) 95%CI
Total n (%) 95%CI
High risk (>60)
Urban
4771 (33.0%) (0.321–0.337) 2886 (19.9%) (0.192–0.205)
3413 (21%) (0.203–0.216) 2746 (16.9%) (0.146–0.158)
3372 (19.2%) (0.186–0.197) 4823 (27.5%) (0.267–0.281)
4160(16.8%) (0.162–0.172) 1749(7.0%) (0.067–0.073)
7544(27.7%) (0.271–0.281) 6088(22.0%) (0.218–0.228)
3513(15.7%) (0.152–0.161) 3234 (14.4%) (0.139–0.148)
8311(29.0%) (0.284–0.295) 5567 (19.4%) (0.189–0.198)
35.084(23.1%) (0.229–0.233) 27,073(17.8%) (0.176–0.180)
2033 (14.0%) (0.134–0.146) 1861 (12.9%) (0.123–0.134)
2349 (14.5%) (0.139–0.150) 2689(16.6%) (0.159–0.171)
1677(9.5%) (0.091–0.099) 3433(19.5%) (0.189–0.201)
3049 (12.3% (0.118–0.127) 3020(12.2%) (0.117–0.125)
3493 (12.8%) (0.124–0.132) 3770(13.8%) (0.134–0.142)
3896 (17.4%) (0.168–0.178) 4117 (18.4%) (0.178–0.188)
6670 (23.3%) (0.227–0.237) 3114 (10.9%) (0.104–0.112)
23,167(15.2%) (0.151–0.154) 22,004(14.5%) (0.143–0.146)
Urban
1556 (10.7%) (0.102–0.112)
2057 (12.7%) (0.121–0.131)
1207(6.9%) (0.065–0.072)
4843(19.5%) (0.190–0.200)
2804(10.3%) (0.099–0.106)
4065(18.1%) (0.176–0.186)
2667(9.3%) (0.089–0.096)
19,199(12.6%) (0.125–0.128)
Rural
1371 (9.5%) (0.089–0.099)
2985 (18.4%) (0.177–0.189)
3055 (17.4%) (0.168–0.179)
7996 (32.2%) (0.316–0.328)
3657 (13.4%) (0.130–0.138)
3583 (16.0%) (0.155–0.164)
2358 (8.2%) (0.079–0.085)
25,005(16.5%) (0.163–0.166)
14,478
16,238
17,567
24,817
27,276
22,408
28,687
1,51,532
Rural Moderate risk(30–50)
Urban Rural
Low risk (<30)
Total
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All India
Area
J&K (NW) n (%) 95%CI
NE n (%) 95%CI
North n (%) 95%CI
Central n (%) 95%CI
West n (%) 95% CI
East n (%) 95% CI
South n (%) 95%CI
=Total n (%) 95%CI
High risk (>60)
Urban
634 (62.9) (0.598–0.659) 273 (27.1) (0.243–0.299) 907 (8.01%)
592 (45.7) (0.430–0.485) 295 (22.8) (0.205–0.232) 887 (7.8%)
469 (32.5) (0.300–0.349) 723 (50.1) (0.474–0.527) 1192(10.53%)
722 (49.7) (0.471–0.523) 266 (18.3) (0.163–0.204) 988 (8.73%)
1490 (48.0) (0.462–0.497) 1094 (35.2) (0.335–0.369) 2584(22.84%)
669 (37.1) (0.365–0.411) 553 (30.7) (0.285–0.329) 1,222 (10.80)
2401 (55.0) (0.535–0.565) 1101 (25.2) (0.239–0.265) 3502 (30.95)
7007 (48.3) (0.47–0.49) 4305 (29.7%) (0.29–0.30) 11,312
55 (5.4) (0.04–0.07) 29 (2.87) (0.019–0.041) 84
202 (15.6) (0.136–0.177) 121 (9.35) (0.078–0.110) 323
91 (6.3) (0.05–0.07) 103 (7.1) (0.058–0.085) 194
162 (11.1) (0.095–0.128) 99 (6.8) (0.055–0.082) 261
257 (8.2) (0.073–0.083) 170 (5.4) (0.047–0.063) 427
209 (11.6) (0.101–0.131) 228 (12.6) (0.111–0.142) 437
431 (9.9) (0.09–0.108) 253 (5.8) (0.051–0.065) 684
1407 (9.7%) (0.092–0.102) 1003 (6.9%) (0.065–0.073) 2410
4 (0.39) (0.001–0.01) 12 (1.1) (0.006–0.02) 16
42 (3.2) (0.023–0.043) 41 (3.17) (0.022–0.042) 83
16 (1.1) (0.006–0.017) 41 (2.8) (0.02–0.038) 57
81 (5.5) (0.044–0.068) 122 (8.4) (0.070–0.099) 203
54 (1.7) (0.013–0.022) 39 (1.25) (0.008–0.017) 93
83 (4.6) (0.036–0.056) 58 (3.2) (0.024–0.041) 141
104 (2.38) (0.019–0.028) 69 (1.58) (0.012–0.019) 173
384 (2.69%) (0.023–0.029) 382 (2.62%) (0.023–0.029) 766
1007
1293
1443
1452
3104
1800
4359
14,458
Rural Total Moderate risk(30–50)
Urban Rural Total
Low risk (<30)
Urban Rural Total
Zone wise Total
Note: >95% of self reported diabetics were in high and moderate scores on IDRS.
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Table 4 – Distribution of known Diabetes patients in different ranges of risk scores on IDRS in Rural and Urban sectors of different zones of India.
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6
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Table 5 – Age wise distribution of IDRS in known diabetes patients in the Screened population. Age range
IDRS High
Moderate
Low
Total number
Known DM
Total number
Known DM
Total number
Known DM
<20 20–29 30–39 40–49 50–59 60–69 70–79 >80
196 1810 9045 15,558 16,617 11,191 2408 262
18 (0.5%) 112 (0.4%) 643 (1.9%) 2336 (7.6%) 3959 (17.4%) 3355 (22%) 737 (22.8%) 75 (19.8%)
793 8421 12,806 10,149 4824 3310 672 91
12(0.3%) 108 (0.4%) 363 (1.1%) 778 (2.5%) 566 (2.5%) 438 (2.9%) 110 (3.4%) 14 (3.7%)
2940 16,811 12,343 4858 1296 731 152 25
26 (0.7%) 161 (0.6%) 199 (0.6%) 185 (0.6%) 93 (0.4%) 67 (0.4%) 11 (0.3%) 3 (0.8%)
Total
57,087
11,215
41,066
2376
39,156
745
Table 6 – Sensitivity and Specificity of IDRS. Criterion
Sensitivity
95% CI
Specificity
95% CI
+LR
95% CI
0 >0 >10 >20 >30 >40 >50 >60 >70 >80 >90 >100
100.00 99.54 99.11 97.99 94.70 88.66 78.05 61.32 40.74 17.64 4.11 0.00
100.0–100.0 99.4–99.6 98.9–99.3 97.7–98.2 94.3–95.1 88.1–89.2 77.4–78.7 60.5–62.1 39.9–41.5 17.0–18.3 3.8–4.4 0.0–0.03
0.00 3.14 8.81 18.24 31.24 45.13 62.68 76.50 88.27 96.45 99.49 100.00
0.0–0.003 3.0–3.2 8.7–9.0 18.0–18.5 31.0–31.5 44.9–45.4 62.4–62.9 76.3–76.7 88.1–88.5 96.3–96.6 99.5–99.5 100.0–100.0
1.00 1.03 1.09 1.20 1.38 1.62 2.09 2.61 3.47 4.97 8.12
1.0–1.0 1.0–1.0 1.1–1.1 1.2–1.2 1.4–1.4 1.6–1.6 2.1–2.1 2.6–2.7 3.4–3.6 4.7–5.2 7.3–9.1
LR
95% CI
0.15 0.10 0.11 0.17 0.25 0.35 0.51 0.67 0.85 0.96 1.00
0.1–0.2 0.09–0.1 0.10–0.1 0.2–0.2 0.2–0.3 0.3–0.4 0.5–0.5 0.7–0.7 0.8–0.9 1.0–1.0 1.0–1.0
Table 7 – a & b: Regression analysis showing prediction of self reported diabetes by IDRS. a) Multinomial regression Parameter Estimates IDRS3riskfcatora
1.00
Intercept [PreRdiabetes [PreRdiabetes Intercept [PreRdiabetes [PreRdiabetes
2.00
B
= 0.0] = 1.0] = 0.0] = 1.0]
Std. Error
2.597 0.988 0b 1.100 0.410 0b
0.158 0.165 . 0.083 0.090 .
Wald
269.822 35.747 . 174.425 20.787 .
df
Sig.
1 1 0 1 1 0
Odds ratio
<0.001 <0.001 . <0.001 <0.001 .
95% CI for Exp (B) Lower Bound
Upper Bound
2.686 .
1.943 .
3.713 .
1.507 .
1.264 .
1.798 .
a. The reference category is: 3.00. b. This parameter is set to zero because it is redundant. b) Binary logistic regression Variables in the Equation B
a
Step 1
IDRS3riskfcator IDRS3riskfcator(1) IDRS3riskfcator(2) Constant
0.578 0.988 2.489
S.E.
0.177 0.165 0.159
a. Variable(s) entered on step 1: IDRS3riskfcator.
Wald
49.774 10.708 35.747 245.927
df
2 1 1 1
Sig.
<0.001 0.001 <0.001 <0.001
Odds ratio
1.782 2.686 0.083
95% C.I.for EXP (B) Lower
Upper
1.261 1.943
2.519 3.713
diabetes research and clinical practice
60–69 (22%); 70–79 (22.8%); >80 (19.2%). Age wise distribution has been shown in Table 5
2.2.
Sensitivity & specificity of IDRS
ROC curve was plotted for 137,947 participants. Area under the ROC curve was found to be 0.763 at CI 95% of 0.761 to 0.765 with statistical significance of p < 0.0001. Youden index at >50 criterion, the sensitivity of 78.05 and specificity of 62.68 was observed. Sensitivity and specificity at different criterion have been provided in Table 6. Prediction of self reported diabetes through IDRS was found to be positively significantly associated with odds ratios 1.782 (1.261–2.519) and 2.686 (1.943–3.713) as provided in Table 7.
3.
Discussion
IDRS is one of the cost-effective methods to detect the DM risk among the Indian population.[8,16] This is the first nationwide study on 240,000 population conducted within 3 months in all zones of India. Based on IDRS data, we report that 40.9% & 29.7% of known DM subjects fall in high risk and moderate risk groups, respectively. This increased to 78.1% among the known subjects. However, North western J&K and south Indian zones were found to be affected. In a Lucknow based study, conducted on 272 subjects, 67.2% were found to be high risk. [17] Similar studies reported high risk populations of 43% [18] and 19% in the rural Tamil nadu [10]. Undoubtedly, IDRS has emerged as a sensitive tool to detect undiagnosed Diabetic subjects, though the sensitivity and specificity scores varied in various studies. Dudega et al reported a sensitivity of 95.12% and specificity of 28.95% at the cutoff score of >60. [12] Similarly, Adhikari et al reported best sensitivity (62.2%) and specificity of (73.7%) at the cut off IDRS score of 60[8]. However, a large study based on 26,000 subjects identified IDRS detection sensitivity and specificity to be 72.5% and 60.1% respectively (for determining undiagnosed diabetes). [7]. Diabetes prediction scales have been developed in various other populations [19,20]. Different diabetes risk scores including FINDRISC [21], DANISH[22], DESIR[23], ARIC[24] and QDScore[25] have also been used for predicting diabetes in different populations. We observed that females are at a higher risk for development of DM with the highest number of cases in North Indian females [26] as compared to males. Previous studies, based on different zones, have reported mixed results where some researchers indicated gender differences [27,28] while others found it more prevalent in females [29] or males [30–32]. Geographical distribution of DM showed a maximum number of Diabetic subjects in west and south zone and the zone with the lowest number of Diabetic incidence was Central followed by Eastern zone. A study done by Agarwal and Ebrahim seeking to screen the variations in Diabetes prevalence in different geographical regions in India, reported maximum incidence of Diabetic subjects in south Indian states like Kerala and Goa in comparison with the central zone state like Rajasthan [31]. The MDRF–IDRS is considered a simple tool of assessment, since a non-physician may collect the data based on age,
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family history, physical activity and a single measurement of waist circumference. Moreover, its accuracy strengthens the utility for screening Diabetic subjects[33] especially in India where more than 41 million are suffering with Diabetes while majority among these are unaware of it. IDRS is thus a good screening tool before carrying out the blood sugar test in the population. Further, the risk assessment using IDRS score has revealed that more than half of Indian population (55.7%) falls under high risk of developing DM. Therefore, a big increase in the Diabetes subjects in India in near future is expected. This may be partly due to higher proportion of population falling in the middle and old age group. Similar rising trends of DM in India have been reported by other studies [34]. Other studies have similarly used IDRS for screening high risk population and found about 41% [7] and 31.5% [35] population to fall under this category. Females were reported to have higher risk of DM incidence than males. Besides, age was found to be a strong risk factor for its occurrence [28]. It was found that DM incidence is expected to increase in South, North and West Zones in near future. Ominously, DM is spreading very rapidly in Indian population which requires immediate preventive public health initiatives like multiple educational and awareness programs in the direction for prevention and amelioration of DM. Conclusion: IDRS score distribution showed higher prevalence of DM patients falling into high risk group. >50 cut off youden index showed the sensitivity of 78.05 and specificity of 62.68 which approves the utility of IDRS as a cost effective tool. IDRS was found to be a strong predictor for cases with diabetes. IDRS tool based on this study findings have public health implications. Moreover method can be utilized by the practicing clinicians in early diagnosis of DM.
Funding This research was funded by Central Council for Research in Yoga and Naturopathy (CCRYN), New Delhi (Ref F.No. 16-63/2 016-17/CCRYN/RES/Y&D/MCT/Dated: 15.12.2016).
Declaration of Competing Interest None exists.
Acknowledgement We acknowledge Ministry of AYUSH, Govt of India, New Delhi, for funding this project. We also acknowledge support of CCRYN for manpower, MOHFW for supporting the cost of investigations and IYA for the overall project implementation. We thank the advisory research committee, senior research fellows, Mr Sabzar, Dr Sanjay, Ms Radhika, Dr Sunanda Rathi, Yoga volunteers and the President of Indian Yoga Association for their contribution in this project.
Author contributions R.N. and H.R.N. Conceptualization, Data Curation and acquisition, Funding Acquisition, Supervision R.N. and A.S. Formal Analysis, Investigation, Methodology, Validation and Writ-
8
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ing—review and editing. R.T. and P.B. Drafting of the Original draft, critical review and editing. A.A. Concept of manuscript.
Appendix A. Supplementary material
[16]
Supplementary data to this article can be found online at https://doi.org/10.1016/j.diabres.2020.108088. [17] R E F E R E N C E S
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[35] Chowdhury R. MAaKLS, A study on distribution and determinantsof indian diabetic risk score (idrs) among rural population of west bengal ISSN: 2249 4995|eISSN: 2277 8810. A study on distribution and determinantsof indian diabetic risk score (idrs) among rural population of west bengal. Natl J Med Res 2012;2:282–6. [36] Kaveeshwar SA, Cornwall J. The current state of diabetes mellitus in India. Austral Med J 2014;7(1):45–8. https://doi.org/ 10.4066/AMJ.2013.1979.