Prevalence of prediabetes, and diabetes in Chandigarh and Panchkula region based on glycated haemoglobin and Indian diabetes risk score

Abstract There is a rapid increase in the prevalence of diabetes in India. We wanted to review the status of prediabetes and diabetes in the combined population of Chandigarh and Panchkula region based on both Indian Diabetes Risk Score (IDRS) and Glycated Haemoglobin (HbA1c). A total of 1215 subjects were recruited during the screening process, out of which 444 i subjects have been analysed for the current study on the basis of high risk for IDRS (≥60) and their known diabetes status. This study included 431 subjects having high risk for IDRS (≥60) and 13 known subjects with diabetes (IDRS < 60) which were further analysed for biochemical and anthropometric parameters. The prevalence of diabetes was found to be 12.67% and prediabetes 11.69% in the combined population of Chandigarh and Panchkula. There was an increased level of fasting blood glucose (183.12 ± 68.61), postprandial blood glucose (262.57 ± 96.92), triglyceride (193.84 ± 119.88), very low‐density lipoprotein (VLDL) (34.87 ± 15.42) and High Density Lipoprotein(HDL) (4.61 ± 1.39) in the said diabetes population. Mean HDL was found to be decreased in subjects having diabetes. Glucose‐induced lipid intolerance study revealed significant alteration in triglyceride, HDL and VLDL. The study has revealed that high prevalence of diabetes in the sampled population when compared with the national average of 8.8%.


| INTRODUC TI ON
Percentage of glycated haemoglobin (HbA1c) is used as an important biochemical parameter to assess past three month's blood glucose status. 1  World Health Organization (WHO) recommended that HbA1c as a tool 11.8% for these 4 years. This report has revealed the prevalence of known diabetes cases to be 8%, whereas the prevalence of new cases was 3.8%. 13 The estimates of prevalence and identification of the individuals with high risk for diabetes are important for the planning.
The identification of these high-risk individuals are equally important, and this can only be achieved if they are identified at transition state or before that. Prediabetes can be considered as the transition state between a healthy and a diabetes individual. Prediabetes, also called intermediate hyperglycaemia, is a condition in which the serum blood glucose levels are higher than the normal levels, but not enough to cause diabetes. According to ADA, the cut-off level for prediabetes is 5.7%-6.4%. Prediabetes is linked with the abnormalities in the form of insulin resistance and β-cell dysfunction which starts before glucose changes are measurable. It is estimated that there will be >470 million people with prediabetes in 2030. The conversion rate of prediabetes to diabetes is around 5%-10%. 14 Furthermore, by identification of prediabetes in the population, the threat of conversion from prediabetes into diabetes can be reduced. 15 The Indian Diabetes Risk Score (IDRS) is a method developed by Mohan et al in 2005 to analyse the risk of prediabetes/diabetes at mass level. 16 IDRS considers four parameters: age, family history (father or mother), physical activity and abdominal obesity (waist circumference).
Risk assessment by IDRS involves 3 categories: score < 30 (low risk), 30-50 (moderate risk) and ≥60 (high risk). We performed a cross-sectional study in 2 regions of North West India population (Chandigarh and Panchkula) based on IDRS in order to explore the prediabetes and diabetes individuals in the community further validated on the basis of HbA1c levels. Individuals with high risk (IDRS score ≥ 60) were selected for the analysis. The specificity of IDRS score ≥ 60 was 60.1%, whereas the sensitivity was 72.5%. 17

| Study design
This study was a part of Niyantrita Madhumeha Bharata (NMB) programme, in which 29 Indian States and 7 Union Territories (UTs) were screened. It was a multi-level cluster randomized controlled trial. However, the data presented in this study are of two regions of North West India i.e. Chandigarh (Union Territory), and Panchkula (District). For sample size calculation, we referred to Diabetes Community Lifestyle Improvement Program (D-CLIP) study published in diabetes care. 18 The details are published in our recent publications. 19,20 As a part of this national programme, house-tohouse screening was carried out by trained volunteers of Indian Yoga Association (IYA). A two-page questionnaire was used which comprises of the personal information about name, age, family history of diabetes, waist circumference, height and weight, besides collecting the workout information. Based on this, IDRS score was calculated. 17 Initially, a total of 1215 subjects were recruited for the study, out of which 444 subjects were assessed for the biochemical parameters

Inclusion criteria
• Both male and female participants with diabetes (self-reported, which was cross verified) • Subjects were from within the periphery of 10km distance from rural and urban regions.
• Those individuals who showed an IDRS score ≥ 60 (for further recruitment) • Patients/Subjects who gave their consents for the study.

Exclusion criteria
• Age below 18 years • Patients with cardiac disease and tuberculosis.
• Those who had complex surgeries in past.
• Those who had major illness which may disable an individual having diabetes.
• Individuals with neurological disorders.

| Biochemical
Participants were called for the special blood collection camp on empty stomach with minimum eight hours of fasting. The blood sampling and subsequent biochemical analysis was carried out

| Statistical analysis
The statistical analysis was done by using the IBM SPSS Statistics Version 21. The data are presented as mean, standard deviation (SD) and standard error (SE). Descriptive statistics was performed by one-way ANOVA. The data were found to be statistically significant at P < .05.

| RE SULTS
Based on the house-to-house screening, 1215 individuals were recruited and screened as per IDRS score as low-risk, moderate-risk and high-risk individuals. Out of 1215, a total of 444 subjects were found to be at high risk (≥60 or known diabetes status) which were further assessed according to HbA1c levels. The final prevalence of diabetes, prediabetes subjects were found to be 12.67% and 11.69%, respectively, as depicted in the Table 1. *There were total 13 individuals with IDRS score less than 60, but HbA1c ≥ 6.5.

TA B L E 2 Comparative analysis of various physiological and biochemical parameters in Healthy, Prediabetes and Diabetes individuals with respect to HbA1c
Subgroups (HbA1c) were highest amongst the prediabetes subjects. We measured the glucose-induced lipid intolerance by estimating different lipid parameters with respect to FBG and PPG, considering the reference range of FBG as 70-110 mg/dL and a range of 80-140 mg/dL for PPG. 21 We found that for PPG and FBG, parameters like triglyceride and VLDL showed significant increased (P < .05) in lipid intolerance, whereas HDL showed significant decrease (Figure 2) (Table S1).

| D ISCUSS I ON
In the present study, we have analysed the prevalence of prediabetes, and diabetes subjects based on IDRS score and HbA1c levels. Our study is based on IDRS assessment to diagnose diabetes and prediabetes in the target population. There are very few IDRSbased studies in the target population (Chandigarh and Panchkula).
In previously published study very low sample size (n = 155) was used. 22 Therefore, we wanted to study the prevalence of diabetes and prediabetes in larger sample size (n = 444). According to present study, the prevalence of prediabetes and diabetes was 11.69% and 12.67%, respectively. Clinical parameters such as FBG and PPG are considered as standard parameters for glycaemic index. 23 Therefore, considering these two parameters, we analysed the glucose-induced lipid intolerance. We found significant alteration in case of triglycer- cholesterol transfer from peripheral cells to the liver. HDL also neu- Association (IYA) for overall project implementation. We would also like to thank Divya Dwivedi, Diksha Puri and Debopriya Basu for validating the data.

CO N FLI C T O F I NTE R E S T
Authors declare no conflict of interest.

AUTH O R ' S CO NTR I B UTI O N
SK contributed to data collection, original writing and statistical analysis; AA contributed to concept of manuscript; RN contributed to proposal writing, study design, planning and monitoring; NK contributed to data collection and segregation of the data; MSS contributed to manuscript writing and statistical analysis; VP contributed to writing and editing; DKP contributed to data collection, NM contributed to data collection and monitoring; AKS contributed to planning, monitoring, data management and quality assurance; HRN contributed to vision, concept, proposal, planning, monitoring, advice, problem solving and editing.

DATA AVA I L A B I L I T Y S TAT E M E N T
All the associated data is available within the article in the form of