Abstract
The present study is aimed to develop the database on glycemic carbohydrates, glycemic index and glycemic load of commonly consumed breakfast foods of South India. Twenty-three varieties of commonly consumed breakfast foods of South India were tested. The data on glycemic carbohydrates were developed by using a modified method of anthrone followed by glycemic index and glycemic load by using FAO/WHO method. The results of glycemic carbohydrates among the commonly consumed breakfast foods range from 49.63% (vada sambar) to 71.84% (vegetable biryani). The results of the glycemic index among the commonly consumed breakfast foods were shown highest of 79.69 (onion dosa) and lowest of 36.89 (vada sambar). The results of the glycemic load of commonly consumed breakfast foods tested were shown highest of 39.69 (plain dosa) and lowest of 18.44 (vada sambar) respectively. The glycemic carbohydrates, glycemic indices and glycemic loads among the breakfast foods tested were almost similar except for vada sambar. To our knowledge, this is the first study to report glycemic carbohydrate, glycemic index and glycemic load of commonly consumed breakfast foods of South India and found to be higher in rice-based breakfast foods than that of legume-based breakfast foods.
Keywords: Carbohydrate, Anthrone method, Glycemic index, Glycemic load, Breakfast foods
Introduction
The metabolic syndromes, such as obesity and type 2 diabetes, are worldwide health problems. The prevalence of metabolic diseases is associated with dynamic changes in dietary macronutrient intake during the past decades (Nakamura et al. 2012). Many preliminary studies suggest that the macronutrient composition in the diet plays an important contributory role in Obesity (Scarborough et al. 2011).However, the role of the individual macronutrients in the development of Obesity remains controversial (Grech et al. 2018). Dietary energy is basically provided by macronutrients, including fats, carbohydrates, and proteins, and the cause of weight gain is energy imbalance (WHO Technical Report Series 916. Report of a Joint WHO/FSA Expert Consultation 2004). The Obesity, especially central Obesity and increased visceral fat due to physical inactivity and consumption of high-energy or high-fat and high sugar diets, are major contributing factors (Mohan 2004). Consumption of imbalanced macronutrients leads to weight gain and brings about risk of non communicable diseases, which kills nearly 3 million people worldwide, per year (Pereira-Miranda et al. 2017).
Carbohydrates play a major role in the human diet, comprising about 40–85% of energy intake. The quality of carbohydrates has been heavily debated in recent years (Carbohydrates in human nutrition. Report of a Joint FAO/WHO Expert Consultation 1998). The debate regarding the optimal amount of dietary carbohydrate has intrigued scientists and policymakers for decades and does not seem to be close to an end (Ludwig et al. 2018). Unfortunately, very often, the dispute does not consider the different health effects of the various carbohydrate foods; this can lead to an inaccurate interpretation of the epidemiological evidence, thus inappropriately promoting the reduction of all carbohydrate-rich foods in the habitual diet (Dehghan et al. 2017). The endorsement of carbohydrate quality by health professionals and policymakers will contribute to the transformation of the global food system towards the provision of healthful and sustainable foods for all the present and future inhabitants of the earth (Willett et al. 2019).
In India, the bulk of calories (60–70%) come from carbohydrate-containing foods. The glycemic carbohydrates provide carbohydrates to body cells, mainly in the form of glucose. The total carbohydrate in food should be determined by direct measurement rather than ‘by’ difference (Kirpitch and Maryniuk 2011).
Dietary prescribing in diabetes mellitus based on the glycemic index (GI) of foods has for all time, a fevered notion. Food choices with lower glycemic index were prescribing in diabetes based on the glycaemic index of the foods has always been an attractive concept. Food choices with lower GI are linked with lower glucose response after use and hence would be the favored choice for patients of diabetes. This idea was very promising; the real challenge over the years has been its implementation in day-to-day practice (Madhu 2017). The glycemic index was proven to be a more useful nutritional concept than the chemical classification. The current criticisms of GI and why GI is valid are the following: (a) GI testing methodology is literal and precise enough for practical use (b) GI is a property of foods (c) GI is biologically meaningful and relevant to virtually everyone (Brouns et al. 2005). GI concept is a novel concept from a regulatory point of view, and a number of problems needed to be addressed to successfully translate GI knowledge into practice (Brouns et al. 2005).
In spite of all the limitations, the glycemic index continues to detain the concentration of physicians and nutritionists the same as it does offer a rational way of ranking carbohydrate-containing foods that has the potential to favorably affect the avoidance and supervision of diabetes mellitus. However, one should exercise caution and refrain from using GI as the sole basis for diabetic diet prescribing. The Glycemic index can also be used sensibly along with other nutritional health requirements to the patient to further enhance the quality of the food choices and improve diabetes management (Madhu 2017).
The dietary management of diabetes requires a sound knowledge of blood glucose as well as insulin responses to meals as the treatment targets reduction of postprandial hyperglycemia and hyperinsulinemia. The use of the glycemic index concept in meal planning can improve diabetes control and other health parameters. Understanding the benefits of the glycemic index one can implement it into the diet allows health care practitioners to educate patients about its use (Monro and Burlingame 1996). Accurate estimates of the glycemic index have been historically challenging in nutritional epidemiology because of database limitations (Casterline et al. 1999).
The prevalence of diabetes is rising in excessive magnitude, and the study conducted among urban subjects by [National Urban Diabetes Survey (NUDS)] the prevalence of diabetes in the southern part of India was found to be higher than the northern and western India (Ramachandran et al. 2001).
Most of the Indian dishes are made up of rice and wheat. The consumption of rice is profound with vegetables and dal as well as chutney. The breakfast foods such as Idlis (steamed rice cakes) and Dosas (a type of pancake) are made of rice and dal. Upma (a type of porridge) is also rice-based breakfast. Sambar (the type of liquid soup) is consumed with most of the breakfast foods along with coconut chutneys (Krishnakumar 2019).
The database on the glycemic carbohydrate and glycemic index/load of the commonly consumed Indian breakfast foods are scanty in the literature. Therefore, the aim of the present study was to carry out for the development of a database on glycemic carbohydrate that is digestible in the human upper gastrointestinal tract by using enzymes that mimic the human system under laboratory conditions using a modified method of anthrone and the glycemic index/load by using the FAO/WHO method (Carbohydrates in human nutrition. Report of a Joint FAO/WHO Expert Consultation, 1998).
Materials and methods
Sampling
It is a cross-sectional study with multi-stage random sampling procedures applied to collect the food samples in the Greater Hyderabad Municipal Corporation (GHMC) area. There are 150 wards in this area and the area is divided into 5 regions (east, west, north, south and central), from each region 5 wards (total 25 wards) and 1 sub area was selected from each selected ward for collection of commonly consumed ready to eat breakfast foods. From each location 6 similar breakfast foods was collected at random for which the listing of locations and breakfast foods enumerated before the sample collection. The listing also provides the information on the commonly available ready to eat breakfast foods in the selected location for the development of a database on glycemic carbohydrates and glycemic index/load.
Sample preparation
The individual items of each breakfast foods were composed and mixed in a mixture grinder and dried in the oven at 500C for 36 h, and the dried sample was then milled to flour and the flour was used to develop the database of the glycemic carbohydrate.
Sugars
Glucose (> 99.5% purity; Sigma Chemical Co., St. Louis, MO, USA) was used in this study.
The standard glucose Stock solution is 100 mg in 100 mL of distilled water.
Working standard Ten milliliters of stock solution was diluted to 100 ml with distilled water (100 μg/mL).
Anthrone reagent
Two hundred milligrams of anthrone was dissolved in 100 ml of ice-cold sulphuric acid.
Enzymes
Total Dietary Fiber Kit (Sigma, TDF-100A) was used. This kit includes 10 mL heat-stable α-amylase, 500 mg protease, and 30 mL amyloglucosidase.
Phosphate buffer
0.08 M, pH 6.0. Dissolve 1.400 g anhydrous dibasic sodium (Na2HPO4) and 9.68 g monobasic sodium phosphate monohydrate (NaH2PO4. H2O) in 1 L water. Check the pH level and adjust if necessary.
NaOH 0.275 N. Dissolve 11.00 g NaOH in 1 L water.
HCL 0.325 M. Dilute 325 mL 1 M HCL to 1 L with water.
Estimation of glycemic carbohydrates
Triplicate test portions of commonly consumed breakfast food samples were treated with heat-stable α-amylase, protease, and amyloglucosidase in order to hydrolyze proteins and starch under laboratory conditions, as given in the following:
Food samples (100 mg) were taken into 16 × 125 mm tubes with screw caps in duplicate. Five milliliters of (0.08 M) phosphate buffer pH 6.0 was added to the tubes and stored at 4 °C for 12 h for hydration of the matrix. After 12 h, hydration samples were subjected to enzyme hydrolysis to degrade soluble starch. The- amylase solution (50 μL) was added, and the tubes were placed in a water bath at 95 °C for 30 min. After 30 min, the tubes were removed and cooled to 60 °C and adjust to pH 7.5 with 1 ml of 0.275 M NaOH. Protease solution (50 μL) was added to the test tubes and then incubated at 60 °C for 30 min. After that, 1 ml of 0.325 M HCl was added to the tubes to decrease the pH to 4.5. After adjusting the pH, amyloglucosidase solution (150 μL) was added and then incubated at 60 °C for 30 min. The residue was separated by centrifugation. The liquid portion was placed to a 100-ml volumetric flask and made up to the mark with deionized water. The concentration of glycemic sugars in the supernatant was determined by using anthrone reagent. Different volumes of supernatant, 0.2–1 ml into a series of test tubes were taken, and the volume was made up to 1 ml with distilled water to each tube. Four milliliters of anthrone reagent were added, and the tubes were placed in a boiling water bath for 8 min and then cooled rapidly under running tap water. The optical density of green to dark green was measured at 630 nm against a blank, and the concentration of glycemic carbohydrate was calculated using a standard glucose curve.
Glucometer
The glucometer used for the present study was One Touch Ultra 2 Blood Glucose Meter (Life Scam. Inc., CA59035, China). The measuring range was linear between 0.6 and 33.3 mmol/L (10–600 mg/dL). One-Touch Ultra Blue Test Strips, One Touch Ultra pricking device and One Touch Ultra lancet needles were also purchased from Life Scam. Inc., CA59035, China. The glucose powder is used as a standard for the present study (Akhil Healthcare Private Limited, India).
Determination of glycemic index/load
Standard food
Glucose or white bread can be used as standard food. In the present study glucose was used as standard food.
Fifty gram of carbohydrate portion
In the present study we have carried out for the analysis of glycemic carbohydrates that are digestible in the human upper gastrointestinal tract by using enzymes that mimic the human system under laboratory conditions using modified method of anthrone. 50 g of carbohydrate was compensated using the glycemic carbohydrate database.
Subjects and ethical clearance for the study
Healthy, non-diabetic individuals (n = 10) aged between 20 and 30 years and not under any medication with a BMI range of 21 ± 3 kg/m2 have participated in the study. Subject was studied on separate days in a morning after 10 to 12 h overnight fasting. A standard drink of water tea or coffee should be given with each test meal. The standard glucose served for three times for total of 7 test in random order on separate days fallowed by test food on fourth day. The glycemic index study was conducted as a randomized crossover study. The written informed consent was obtained from each individual. Ethical clearance was obtained from the Institutional Ethics Committee (ICMR-NIN, Hyderabad).
Estimation of whole blood glucose concentrations
The fingertips were pricked using the One Touch Ultra lancet device following 10 to 12 h fasting. The first drop of blood will be placed onto the strip, and a reading was taken (within 5–10 s) and recorded. The 50 g of standard glucose was given; after 15 min of glucose solution, blood glucose readings of different time intervals at 15, 30, 45, 60, 90 and 120 min intervals after taking the first bite were recorded for three days and followed by food for the fourth day.
Calculation of GI
Blood glucose curves were constructed from blood glucose values for each individual at 0–120 min for the control and test foods of each group. The incremental areas under the blood glucose response curve (IAUC) for a 50 g carbohydrate portion of each test food and control food (glucose) were calculated by the trapezoidal rule (modified FAO/WHO) (Carbohydrates in human nutrition. Report of a Joint FAO/WHO Expert Consultation 1998). The GI values were calculated by the method of modified FAO/WHO (Carbohydrates in human nutrition. Report of a Joint FAO/WHO Expert Consultation. 1998). Values were expressed as mean and standard deviation. The blood glucose response curve of test foods and the standard of each individual were calculated. The GI was calculated as a ratio between IAUC of the test to that with the standard of the same individual (Carbohydrates in human nutrition. Report of a Joint FAO/WHO Expert Consultation. 1998).
Calculation of GL
The glycemic load of food is a number that estimates the amount of food will raise a person’s blood glucose level after eating it. One unit of glycemic load approximates the effect of consuming one gram of glucose. The glycemic load was calculated by using the following formula: (Grams of carbohydrate in the food (serving size) × GI of the food)/100.
Statistical analysis
The Glycemic carbohydrate results were expressed as mean values ± standard deviations of three separate determinations. The Glycemic index and glycemic load data were subjected to a repeated measure of one way Analysis of Variance (ANOVA), and the significance of difference between means at 5% was determined by using SPSS (Statistical package for Social Science) version 19.0.
Results
The glycemic carbohydrate content results of commonly consumed Indian breakfast foods are presented in Table 1 and Fig. 1. From the table, it is clear that many varieties of breakfast foods prepared and eaten in southern parts of the country are legume, wheat, cereal and rice based breakfast foods. The percent of glycemic carbohydrate showed highest among the legume based breakfast food is 66.26% in MLA upma pesarattu followed by 65.75% in pesarattu, 56.99% in bisibele bhath and 49.63% in vada sambar respectively. The glycemic carbohydrates of wheat based foods was shown highest of 70.38% in mysore bonda and lowest of 63.50% in parrota among the tested foods. Glycemic carbohydrates content of cereal based breakfast foods were shown significant differences (p < 0.05) and the percent of glycemic carbohydrates was shown highest of 70.95% in onion rava dosa and lowest of 58.98% in idly sambar respectively. The percent of glycemic carbohydrates of rice based breakfast foods ranges from 61.49% in tomato bhath and 71.84% in vegetable biryani among the tested foods.
Table 1.
Glycemic carbohydrate content of commonly consumed breakfast foods of South India
| Sl. No | Breakfast foods | Glycemic carbohydrates (g/100 g) |
|---|---|---|
| 1 | Idly sambar | 58.98 ± 0.0 |
| 2 | MLA Upmapesarattu | 66.26 ± 2.7 |
| 3 | Onion ravadosa | 70.95 ± 0.5 |
| 4 | Open dosa | 70.33 ± 5.7 |
| 5 | Paneerdosa | 68.69 ± 0.6 |
| 6 | Pesarattu | 65.75 ± 0.1 |
| 7 | Ravapaneerdosa | 65.17 ± 3.1 |
| 8 | Set dosa | 69.93 ± 0.2 |
| 9 | Vegetable dosa | 69.56 ± 2.9 |
| 10 | Vadasambar | 49.63 ± 1.5 |
| 11 | Onion dosa | 69.96 ± 0.3 |
| 12 | Plain dosa | 70.75 ± 0.3 |
| 13 | MLA Dosa | 70.13 ± 0.5 |
| 14 | Bisibelebhath | 56.99 ± 0.2 |
| 15 | Open veg paneerdosa | 66.34 ± 0.9 |
| 16 | Tomato bhath | 61.49 ± 2.0 |
| 17 | Lemon rice | 70.36 ± 0.1 |
| 18 | Chapati | 66.12 ± 2.2 |
| 19 | Tomato rice | 71.35 ± 0.2 |
| 20 | Veg biryani | 71.84 ± 3.1 |
| 21 | Curd rice | 70.96 ± 0.7 |
| 22 | Parota | 63.50 ± 1.3 |
| 23 | Mysore bonda | 70.38 ± 0.1 |
Each value is the average of triplicate determinations ± , one SD
Fig. 1.
Glycemic carbohydrate, Glycemic index and glycemic load of commonly consumed breakfast foods of South India
Glucose concentrations obtained from the commonly consumed Indian breakfast foods were used to calculate the incremental area under curves (IAUC) for test foods and standard glucose. The Glycemic index of the commonly consumed breakfast foods was calculated as a ratio by using IAUC of the test food and standard glucose values. The commonly consumed breakfast foods of serving sizes contain 50 g of glycemic carbohydrate. The results of the glycemic index (GI) and glycemic load (GL) of commonly consumed Indian breakfast foods are presented in Table 2 and Fig. 1. From the table, it is evident that legume based breakfast foods have shown significant difference among the foods. The levels of glycemic index were shown highest of 74.64 in bisibele bhath fallowed by 72.85 in MLA upama pesarattu, 60.69 in pesarattu and 36.89 in vada sambar respectively. The glycemic load of legume based breakfast foods was also showed significant difference (p < 0.05). The levels of glycemic load showed highest of 36.42 in MLA upma pesarattu and the lowest of 18.44 in vada sambar. The glycemic index and glycemic load of wheat based breakfast foods showed highest of 62.48 and 31.24 in parota and the lowest of 61.41 in mysore bonda and 28.37 in chapatti among the wheat based breakfast foods. The glycemic index of cereal based breakfast foods shown highest of 79.69 in onion dosa and lowest of 63.97 in vegetable dosa among the cereal based breakfast foods tested and the glycemic load was shown highest of 39.84 in onion dosa and the lowest of 31.98 in vegetable dosa. The glycemic index of rice based breakfast foods was shown highest of 79.30 in lemon rice and the lowest of 64.94 in curd rice and the glycemic load was shown highest of 39.65 in lemon rice and the lowest of 32.47 in curd rice.
Table 2.
Glycemic index and glycemic load of commonly consumed breakfast foods of South India
| Sl No | Breakfast foods | GI | GL |
|---|---|---|---|
| 1 | Idly sambar | 68.69 ± 5.8 | 34.34 ± 7.1 |
| 2 | MLA Upmapesarattu | 72.85 ± 5.8 | 36.42 ± 6.7 |
| 3 | Onion ravadosa | 66.43 ± 5.7 | 33.21 ± 5.3 |
| 4 | Open dosa | 77.33 ± 5.7 | 39.34 ± 3.5 |
| 5 | Paneerdosa | 71.47 ± 4.3 | 35.73 ± 3.7 |
| 6 | Pesarattu | 60.69 ± 5.7 | 33.70 ± 9.5 |
| 7 | Ravapaneerdosa | 71.94 ± 6.2 | 35.97 ± 5.2 |
| 8 | Set dosa | 65.97 ± 5.7 | 32.98 ± 6.5 |
| 9 | Vegetable dosa | 63.97 ± 5.7 | 31.98 ± 7.4 |
| 10 | Vadasambar | 36.89 ± 5.7 | 18.44 ± 7.7 |
| 11 | Onion dosa | 79.69 ± 5.9 | 39.84 ± 4.8 |
| 12 | Plain dosa | 79.39 ± 6.8 | 39.69 ± 2.7 |
| 13 | MLA Dosa | 71.17 ± 6.6 | 35.58 ± 5.4 |
| 14 | Bisibelebhath | 74.64 ± 5.8 | 32.59 ± 5.6 |
| 15 | Open veg paneerdosa | 70.98 ± 6.4 | 35.49 ± 6.8 |
| 16 | Tomato bhath | 68.57 ± 5.8 | 36.54 ± 7.3 |
| 17 | Lemon rice | 79.30 ± 5.9 | 39.65 ± 3.9 |
| 18 | Chapati | 62.43 ± 6.1 | 28.37 ± 5.3 |
| 19 | Tomato rice | 68.89 ± 6.2 | 34.44 ± 7.3 |
| 20 | Vegetable biryani | 74.53 ± 6.1 | 37.26 ± 7.3 |
| 21 | Curd rice | 64.94 ± 5.6 | 32.47 ± 7.5 |
| 22 | Parota | 62.48 ± 5.6 | 31.24 ± 6.3 |
| 23 | Mysore bonda | 61.41 ± 5.6 | 30.70 ± 5.7 |
Each value is the average of ten participant’s determinations ± , one SD
GI Glycemic index, GL Glycemic load
Discussion
For the first time, we are reporting the glycemic carbohydrates content of commonly consumed Indian breakfast foods, which are digestible in the human gastrointestinal tract by using enzymes that mimic the human system by using modified Anthrone method. In the earlier study we have reported the glycemic carbohydrate content in different varieties of rice, vegetables and legumes using the same method and compared with HPLC using refractive index detector and the results of glycemic carbohydrate content with both methods were shown almost similar (Devindra et al. 2019) and similarly we have also reported the glycemic carbohydrates of commonly consumed foods such as rice, wheat, barley, red gram dhal, green gram dhal, Bengal gram whole, masoor dhal, Bengal gram dhal, Wheat + chana dhal (60:40), Wheat + chana + barley (40:30:30), and mixed dhal respectively (Devindra et al. 2019). Southgate (Monro et al. 1996) have suggested that any analytical method for the estimation of glycemic carbohydrates must of necessity represent a compromise between the “ideal” procedure based on the known properties of the carbohydrates and a practical laboratory procedure. Kamath and Belavady (1980) have reported that the importance of glycemic carbohydrates in normal and therapeutic diets. Casterline et al. (1999) have reported that the total carbohydrates of 78.4% to 81.4% in rice cocoa by treating with the same enzymes and analyzed by HPLC using a refractive index detector. Barreira et al. (2010) have shown that sugars profile of different Chestnut and Almond cultivars by HPLC-RI. Ellingson et al. (2010) have developed a new method for the direct determination of glycemic carbohydrates in low-carbohydrate products using high-performance anion-exchange chromatography.
In our earlier study we have developed the database of the glycaemic or available carbohydrate content of different food items. Those values were used to calculate the portion sizes needed for in vivo GI testing against a glucose reference as per recent guidelines of FAO/WHO method. This study is an important addition in this direction. The use of standard methods will ensure that a major reason of variability of GI measurements is eliminated, and we can use these values with more confidence in clinical practice. Ryan et al. (2019) have reported the low glycemic index and glycemic load values of vegetables such as tomato and gungo fruits such as papaya, watermelon and guava of Jamaica. Jimmy et al. (2011) have suggested that it is difficult to examine the relationship between dietary glycemic index as well as glycemic load and the risk of diseases in epidemiological studies due to the lack of a consistent methodology to assign glycemic index values to the food items. Dereje et al. (2019) have developed the database of glycemic index and glycemic load of Ethiopian foods such as Teff Injera, Corn Injera, and white wheat bread. Kingsley Omage and Sylvia Omage (2017) have evaluated the glycemic indices of three commonly consumed mixed meals eaten in Okada; rice and beans (test food 1), rice and plantain (test food 2), beans and plantain (test food 3), and findings show that the selected test foods (mixed meals) consumed in Okada have high GI values. Nagaraju et al. (2020) have reported that the glycemic carbohydrate content, glycemic index and glycemic load of whole-grain base multigrain flour composition 1 (C1) and composition 2 (C2) formulated using defatted soya or Bengal gram as a source of protein along with millets (40 to 45%) and whole cereals. Reynolds et al. (2019) had reported that smaller or no risk reductions of NCDs were found when the glycaemic index or glycaemic load of the habitual diet was considered. In contrast, the meta-analyses study showed a significant reduction in the incidenceof type 2 diabetes mellitus in individuals who consumed a diet with a lower glycaemic index as compared with those who consumed a diet with a higher glycaemic index (Reynolds et al. 2019).
Conclusion
The present study demonstrates that the modified anthrone method is applicable to develop the database of glycemic carbohydrates in different varieties of ready to eat foods, food products and beverages. The present method can be used for routine analysis of glycemic carbohydrates followed by glycemic index/loads. To our knowledge, this is the first study to report glycemic carbohydrate content of commonly consumed breakfast foods of South India, which are digestible in the human gastrointestinal tract by using enzymes mimic the human system in controlled laboratory conditions. The levels of glycemic index and glycemic load of the rice-based breakfast foods are higher than that of legume-based breakfast foods among the tested breakfast foods. The prevalence of diabetes in the southern part of India was found to be higher than the northern and western India from the results it may be concluded that there may be an association of foods consumed with high levels of glycemic index/load. Therefore, when dietary advice is given to diabetic and overweight or obese individuals should very care full.
Abbreviations
- BMI
Body mass index
- IAUC
Incremental area under curves
- GI
Glycemic index
- GL
Glycemic load
- NCDs
Non-communicable diseases
Author contributions
DS designed the study, obtained funding, and wrote the manuscript RN and PPS analyzed the data.
Funding
The source of funding was Indian Council of Medical Research (ICMR), India.
Data availability
All the materials and data pertaining the study are transparent and are available.
Declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent for publication
The authors consent to the publication of the work in the journal.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Devindra Shakappa, Email: dr_devindra@rediffmail.com.
Rakesh Naik, Email: rakesh.naik46@gmail.com.
Prasanthi Prabhakaran Sobhana, Email: psprasanthivenu@gmail.com.
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Data Availability Statement
All the materials and data pertaining the study are transparent and are available.