A study to determine the hidden part of the iceberg of diabetes, using Indian diabetes risk score as a screening tool in rural population of Bangalore, Karnataka, India

INTRODUCTION

Diabetes as a non-communicable disease is a significant public health problem and the prevalence rate is increasing globally and reaching epidemic proportions.¹ It is a leading cause of premature morbidity and mortality and is posing a serious challenge to Indian society and economy.

India is predominantly an agricultural nation with 72.2% of the population living in rural areas. The rural population in India is undergoing drastic lifestyle transition due to socio-economic growth. A study done by Indian Council of Medical Research (ICMR) in the 1970s reported a diabetes prevalence of 1% in rural areas which has increased to 4 -10 % in 2000.² Thus, it is seen that even in rural India, prevalence rates of diabetes is rising rapidly.

The Indian population is known to have an increased susceptibility to develop diabetes mellitus. The ethnicity, presumable genetic vulnerability of Asians, manifests into diabetes when subjected to unfavourable lifestyles.³ In addition to this the changing epidemiological trends, economic boom, reduced physical activity, changing dietary patterns and varied environmental factors also contribute to the increasing risk.⁴

This highlights the need to employ screening programs for early identification of people at the risk of diabetes and also those in the pre-diabetic stage, where simple lifestyle interventions can help prevent or delay the onset of the disease.

Hence, this study was undertaken to access the risk of developing type-2 diabetes and also to detect the undiagnosed cases of diabetes among rural population.

Aims and objectives

• To determine people at risk of developing diabetes using the Indian Diabetes Risk Score (IDRS).

• To calculate the Body Mass Index and measure the Random Blood Sugar levels (RBS) of all the study subjects and classify it according to their risk status.

METHODS

Study design: Community based cross sectional study

Study area: Three pre-selected villages in and around Kakaramanahalli, rural field practice area of RajaRajeswari Medical College.

Study duration: Two months between May and June 2016

Data collection: Using pre designed, semi structured questionnaire based on the Indian Diabetic Risk Score (IDRS)

Sampling method: Convenience sampling

Inclusion criteria: All individuals aged 20 years and above and not known to be diabetics were included in the study after obtaining their consent.

Exclusion criteria: Those individuals who were not willing to participate in the study.

A community based cross-sectional study was conducted in three pre-selected villages in and around Kakaramanahalli, rural field practice area of Department of Community Medicine, Rajarajeswari Medical College. In the three pre-selected villages a house to house survey was conducted. The subjects present in the house at the time of visit were included and were informed about the purpose of the study and a written consent was obtained from all the subjects. At the end of the study we were able to reach 101 individuals

The Indian Diabetes Risk Score was used as the study tool and the general socio demographic profile was also collected along with the score. The components of Indian Diabetic risk score include age, waist circumference, physical activity and family history of diabetes. Minimum Score is 0 and Maximum is 100. Interpretation: score <30- low risk, score 30-50- medium risk, score >60- high risk.⁵

Height and weight measurements were taken and BMI was calculated. The BMI cut off values for Asian Indians as recommended by the WHO was used in the present study. A desirable BMI according to the WHO recommended cut-offs for Asians is considered to be between 18.5 and 22.9 kg/m². A BMI of 23–24.9 kg/m² is defined as “overweight” and ≥25 kg/m² as “obese”. Random Blood Glucose (RBS) was also estimated using a glucometer.⁶

Statistical analysis

Data collected was entered into Microsoft excel sheet and analyzed using SPSS (Statistical package for social sciences) version 16.0. Results were expressed in terms of frequency and percentage. Test of significance was done using chi square. A p-value of <0.05 was considered statistically significant.

RESULTS

A total of 101 subjects formed the study population. The mean age was 52.13±16 years. Minimum and maximum ages were 23 and 90 years respectively. Majority of the study participants were in the age category of 50 years and above. Among study subjects, 33 (32.7%) were males and 68 (67.3%) were females. More than half of the study subjects (52.5%) were illiterates. Greater proportion of the study subjects were Agriculturists. Majority of the respondents were living in joint family (Table 1).

On evaluating the risk status of study subjects for Type 2 Diabetes Mellitus using IDRS, 26 (25.7%) out of the 101 subjects showed low risk score, 49 (48.5%) moderate risk and rest 26 (25.7%) high risk score (Figure 1).

A total of 26 individuals belonged to the high risk group, 4% of them were found to be <35years of age, 15% of them were in the age group of 35-49 years and 81% of them were aged >50years. Thus, it is noted that with increasing age group of the study population the percentage of the individuals belonging to the high risk group also increased. A statistically significant association was seen between the age of the study subjects and the IDRS risk status (p = 0.001)(Table 3).

It was observed that among males and females, who were in high risk Group, 38% of them were males and 62% were females. There was no statistically significant association seen between the gender of the study subjects and the IDRS risk status (p = 0.625) (Table 4).

When the BMI score was assigned according to IDRS risk status, it was found that 51 (50.5%) of subjects had a BMI ≥23, among whom, 16 (31.37%) were under high risk category (IDRS). There was no statistically significant association between BMI Categorization and the IDRS risk Status (p = 0.483) (Table 5).

RBS was measured for all the study subjects irrespective of their risk Status. The mean RBS value of the study subjects was 128±36. Among the subjects, 5(5%) were found to have an RBS value more than the cut off (i.e. >200 mg/dl), of which 2 subjects belonged to high risk group and 2 belonged to medium risk group. These subjects were advised for further investigations to confirm the Diabetes status by Fasting and post prandial blood glucose estimation (Table 6).

DISCUSSION

In this study, we used the Indian Diabetes Risk Score to identify the risk status of undiagnosed diabetics in a rural population. Although various risk factor scoring systems (Ramachandran) were developed previously, IDRS developed by Mohan et al is considered to be one of the strongest predictors of incident diabetes in India.^7,8 IDRS uses two non-modifiable risk factors (age and family history of diabetes) and two modifiable risk factors (waist circumference and physical activity) giving a clear message that if the modifiable risk factors are altered the risk score can be considerably reduced.⁹

The biochemical tests most commonly used to screen diabetes are Fasting blood glucose estimation, 2 hour post prandial blood glucose estimation and oral glucose tolerance test. These tests are labor intensive and expensive to do on a large scale in a community setting. Thus, estimation of Random capillary blood glucose level is the most convenient method as this has the advantage that it can be done at any time of the day and does not need venipuncture.

The current study identified 25.7% of the subjects to be in the high risk category according to IDRS. In a study by Mohan et al. reported that 43% of their study subjects were in high risk group.⁵ In a similar study conducted by Gupta et al, they found 31% of their study population to have a high risk score.² Reason for this difference could be the varied life styles of the study subjects, as our study was conducted in a rural area where as the other studies were done in an urban setting.

In our study it is noted that with higher the age group of the study population, the percentage of the individuals belonging to the high risk group also increased which is similar to the findings obtained by Patil RS et al.¹⁰ Several other studies have recorded similar findings.^11-13

In this study 91% of the study subjects did not have any family history of diabetes. In another study conducted by Patil RS et al 89% of the individuals did not have any family history of diabetes.¹⁰ Gupta SK et al in their study observed that 68.5% of the respondents had no family history of diabetes.² This difference could be due to previous lack of awareness about the disease and lack of screening tests at the community level to detect the cases of diabetes.

Of the 26 subjects who belonged to high risk group according to IDRS, 7% had RBS value >200mg/dl. These findings are concurrent with the results obtained in a study done by Chythra et al, in which 6% of the subjects in high risk group had RBS value of >200mg/dl and belonged to the high risk group.³

CONCLUSION

In conclusion, it is seen that 1/4^th of the subjects in our study belonged to the high risk category. A statistically significant association was seen between the age of the study subjects and the IDRS risk status (p=0.001). There was no statistically significant association seen between the gender of the study subjects and the IDRS risk status (p=0.625). There was no statistically significant association between BMI Categorization and the IDRS risk Status (p = 0.483). Among the subjects, 5 (5%) were found to have an RBS value more than the cut off (i.e. >200 mg/dl). Hence, our study shows that IDRS is a simple, quick and cost effective screening tool for early identification of people at the risk of developing diabetes. Use of IDRS has made mass screening for diabetes more practically feasible.

Recommendations

All the subjects with RBS value more than the cut-off were asked to undergo further confirmatory testing, and those who belonging to IDRS moderate and high risk category were advised to implement lifestyle modifications and dietary changes.

ACKNOWLEDGEMENTS

Authors are grateful to health workers in RHTC, Kaka Ramanahalli and Interns posted under the Department of Community Medicine for their co – operation and help during Data Collection and analysis.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: The study was approved by the institutional ethical committee, RajaRajeshwari Medical College

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