Diabetes mellitus (DM) is a group of metabolic disorders that causes decreased insulin secretion and varying degrees of peripheral insulin resistance that leads to elevated blood glucose levels. ^[1] Symptoms of marked increase in blood glucose levels include polydipsia, polyphagia, polyuria and weight loss (American Diabetes Association, 2024). ^[2]

DM is distinguished by the insulin synthesis deficiency of the pancreas, resulting in chronic hyperglycemia with deficiencies in most metabolic pathways in the body (American Diabetes Association, 2024). DM is one of people's oldest known diseases. ^[3,4] Type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) are commonly used to characterize insulin-dependent and non-idependent diabetes mellitus (American Diabetes Association, 2024). ^[5]

Drug therapies for DM include insulin and numerous oral antidiabetic agents such as sulfonylureas that act by inhibiting the adenosine triphosphate-potassium channel (ATP-K + ) in β-cells of pancreas, causing stimulation of insulin secretion. Biguanides show their action by preventing hepatic gluconeogenesis and increase muscle glucose uptake. ^[6] Thiazolidinediones act by targeting the peroxisome proliferator-activated receptor-gamma (PPAR-γ) that further increase peripheral uptake of glucose by improving insulin sensitivity in the muscle. Meglitinides act by regulating (K+ -ATP) in β-cells of the pancreas thereby increase insulin secretion. ^[7]

Sodium-glucose cotransporter 2 (SGLT2) inhibitors enhance urinary glucose excretion, which consequently reduces hyperglycemia. ^[8] They exert favorable effects on various biomarkers, including blood glucose, body weight, blood pressure, albuminuria, and fatty liver. ^[9] In addition to these effects, several large placebo-controlled cardiovascular and renal outcome trials in patients with type 2 diabetes have revealed that SGLT2 inhibitors may reduce the risk of heart failure, diabetic kidney disease, and cardiovascular death. ^[10]

In contrast, dipeptidyl peptidase-4 (DPP-4) inhibitors decrease glycaemic variability by stimulating glucose-dependent insulin secretion. ^[11] In a previous report, the glucose-lowering efficacy of DPP-4 inhibitors was found to be higher in Asian people than in other ethnic groups. ^[12] Moreover, body mass index (BMI) was significantly correlated with the HbA1c-lowering efficacy of DPP-4 inhibitors, with average BMI < 30 kg/m2 in Asian populations. ^[13] A retrospective cohort analysis of 1.13 million people with type 2 diabetes in Japan reported that DPP-4 inhibitors had been prescribed as the first-line treatment, resulting in DPP-4 inhibitors occupying an overwhelming share of the market compared with other oral hypoglycaemic agents. ^[14]

Dapagliflozin is a selective inhibitor of SGLT2 and acts to reduce hyperglycemia independently of insulin secretion or action. Dapagliflozin reduces systemic glycemic load by inhibiting this transporter, allowing some filtered glucose to pass into the urine for elimination. ^[15] Reduction in HbA_1c with dapagliflozin was relatively consistent across randomized, controlled, clinical trials in a variety of settings from treatment-naive patients to first add-on to metformin, sulfonylurea, or pioglitazone, and to patients requiring insulin, with or without concomitant OADs. ^[16] The blood glucose–lowering effect of dapagliflozin after 6 months of treatment was similar to that of metformin-XR monotherapy and, after 1 year of treatment, was similar to glipizide in patients poorly controlled on metformin monotherapy. ^[17]

This is a Prospective, randomized, Open-label was conducted among Type 2 DM patients attending the outpatient department of Medicine in Index Medical College and Hospital over a period of 2 years.

Inclusion Criteria:

Male or female patients between 40 to 60 years of age. Patients with type 2 diabetes who had not used any glucose-lowering agents within 8 weeks before consenting, or those who had used only metformin; Patients those with HbA1c (NGSP values) levels of ≥ 7.1%. Patients willing to take medications as directed & willing to come for the follow.

Exclusion Criteria:

Patients with history of Alcohol intake & Smoking. Patients with known history of Type 1 Diabetes and hypertension. Patients with severe cardiac, liver and renal disease. Patients with GIT diseases. Patients with a history of lactic acidosis. Patients with hypothyroidism and hyperthyroidism. Patients taking vitamin B₁₂,  folate, steroid, oral contraceptives & hormone replacement therapy. Pregnant and breast-feeding females. Patients with polycystic ovarian disease.

Methodology

All the Type 2 DM patients attending outpatient department (OPD) of Medicine were randomly divided into Dapagliflozin Group and Sitagliptin Group. Subjects willing to participate and written informed consent was obtained from each participant before study. Permission from treating consultant was obtained for subjects to participate in the study. Subjects were screened for selection criteria. Baseline evaluation included recording of demographic details, BMI, medical history, general and systemic examination and laboratory investigations, which included complete haemogram, hepatic and renal function tests and routine urine analysis. The eligible patients were enrolled as randomization.

Treatment:

The treatment drug (dapagliflozin 10 mg/day and sitagliptin 100 mg/day) was administered for 12 weeks. 

Follow-up Visits:

Follow-up visits were scheduled at the end of every month for 12 weeks for assessment, including measurement of weight and general and systemic examination.

Sample collection:

Samples of venous blood was collected from a forearm vein, at baseline and after 12 weeks. Blood samples were centrifuged at 3000 rpm for 15 min to separate the serum, and serum was stored at 4 °C for further assays. 

Biochemical Parameters:

The following laboratory investigation was performed on sample of Type 2 DM patients before and after Dapagliflozin and Sitagliptin therapy.

Statistical Analysis:

The collected data was compiled in MS Excel sheet for analysis in Statistical Package for the Social Sciences (SPSS) version 29^th was applied. The qualitative data was represented in the form of frequencies and percentage also represented in visual impression like bar diagram, pie diagram etc. For quantitative data was represented in the form of mean and standard deviation. To check significance difference between baseline and after three months effect of Dapagliflozin and Sitagliptin group on various parameters level in Type 2 DM patient. A paired ‘t’ test was applied and also quantitative data was represented in the form of pie diagram and bar diagram. p value was check at 0.05 % level of significance.

Graph 1 Baseline characteristics of the study participants

In Graph 1, Mean age among Dapagliflozin group was 59.9 ± 9.5 years and Sitagliptin group 57.8 ± 8.8 years. Whereas, there were no significant differences between two groups. parameters among the two study groups.  In our study 78 were male and 32 were female among Dapagliflozin group and in Sitagliptin group was 76 was male and 34 were female.

Graph 2 Baseline characteristics of the study participants of Body weight and BMI

In Graph 2 body weight was 83.9 ± 9.5 kg in Dapagliflozin group and Sitagliptin group was 81.7 ± 8.15 kg. However, there were no significant differences in the changes in these metabolic parameters among the two study groups. Body mass index (kg/m²) was 26.8 ± 8.8 in Dapagliflozin group and Sitagliptin group was 26.5 ± 9.9.

Graph 3 Effects of dapagliflozin and sitagliptin on HbA1c at 12 weeks

In Graph 3 presents the changes in glycemic and metabolic parameters from baseline to week 12 in the two study groups. The mean ± SD change in HbA1c from baseline to 12 weeks were 1.99 ± 0.98% and 1.75 ± 0.58 in the dapagliflozin and sitagliptin groups, respectively. Although there was no significant difference among the two study groups, the lowering effect of HbA1c tended to be greater in the Sitagliptin group then in the dapagliflozin group.

Graph 4 Effects of dapagliflozin and sitagliptin on Fasting plasma glucose (mg/dl) at 12 weeks

The changes in the time courses of Fasting plasma glucose at baseline and at week 12 in the two study groups are shown in Graph 4. The mean ± SD change in Fasting plasma glucose from baseline to week 12 were 25.7 ± 5.8 and 47.0 ± 8.6 in the dapagliflozin and sitagliptin groups, respectively.

Graph 5 Effects of dapagliflozin and sitagliptin on glycemic Fasting plasma insulin at 12 weeks

In Graph 5 presents the changes in glycemic and metabolic parameters from baseline to week 12 in the two study groups. The mean ± SD change in Fasting plasma insulin from baseline to 12 weeks were 4.14 ± 0.78 and 4.42 ± 0.48 in the dapagliflozin and sitagliptin groups, respectively. Although there was no significant difference among the two study groups, the lowering effect of Fasting plasma insulin tended to be greater in the Sitagliptin group then in the dapagliflozin group.

Graph 6 Effects of dapagliflozin and sitagliptin on glycemic Fasting proinsulin/insulin ratio at 12 weeks

In Graph 6 presents the changes in glycemic and metabolic parameters from baseline to week 12 in the two study groups. The mean ± SD change in Fasting proinsulin/insulin ratio from baseline to 12 weeks were 1.06 ± 0.33 and 1.92 ± 0.28 in the dapagliflozin and sitagliptin groups, respectively. Although there was no significant difference among the two study groups, the lowering effect of Fasting proinsulin/insulin ratio tended to be greater in the Sitagliptin group then in the dapagliflozin group.

Graph 7: Effects of dapagliflozin and sitagliptin on HOMA at 12 weeks

The mean changes in the HOMA- β values from baseline to week 12, as markers of β-cell function, are presented in Table 9. Although there were significant differences from baseline to week 12, the changes in the HOMA- β tended to improve in the two study groups. The mean ± SD change in HOMA-β at 12 weeks from baseline to 12 weeks were 5.9 ± 1.7 and 15.85 ± 0.56 in the dapagliflozin and sitagliptin groups, respectively. The mean ± SD change in HOMA- IR at 12 weeks from baseline to 12 weeks were 1.08 ± 0.03 and 0.68 ± 0.05 in the dapagliflozin and sitagliptin groups, respectively.

In our study, body weight was 81.7 ± 8.3 kg in Dapagliflozin group and Sitagliptin group was 79.5 ± 7.10 kg. However, there were no significant differences in the changes in these metabolic parameters among the two study groups. Body mass index (kg/m²) was 24.9 ± 6.7 in Dapagliflozin group and Sitagliptin group was 24.3 ± 8.8. Several reports on the effects of DPP-4 inhibitors on the glycaemic control of patients stratified by BMI suggest that patients with a lower BMI tend to have better glycaemic control than do those with a higher BMI. ^[18] In addition, a previous systematic review and metaanalysis suggested that DPP-4 inhibitors exhibit a better response to glucose-lowering efficacy in Indian populations than in other ethnic groups. Asian groups tended to have a lower BMI than other ethnic groups. ^[19]

In this study presents the changes in glycemic and metabolic parameters from baseline to week 12 in the two study groups. The mean ± SD change in HbA1c from baseline to 12  weeks were 1.95 ± 0.94% and 2.71 ± 0.54 in the dapagliflozin and sitagliptin groups, respectively. Although there was no significant difference among the two study groups, the lowering effect of HbA1c tended to be greater in the Sitagliptin group then in the dapagliflozin group. However, from these results, it is impossible to determine whether race or baseline is involved in HbA1c improvement. Moreover, another study reported that the relationship between the percentage of Asian participants and the change in HbA1c level from baseline was no longer significant after adjusting for BMI.

In this study, the changes in the time courses of Fasting plasma glucose at baseline and at week 12 in the two study groups are shown in Table 5. The mean ± SD change in Fasting plasma glucose from baseline to week 12 were 123.9 ± 3.4 and 45.0 ± 6.4 in the dapagliflozin and sitagliptin groups, respectively. In fact, FBG was significantly correlated with the HbA1c-lowering efficacy of DPP-4 inhibitors, in which the average FBG. This may be explained by the fact that high insulin resistance in patients with a higher BMI was reduced by treatment with SGLT2 inhibitors. Compared with placebo, SGLT2 inhibitors have recently been shown to improve response. ^[20] However, only a few studies have reported on glucose variability using SGLT2 inhibitors. ^[21]

In our study, dapagliflozin was more effective than sitagliptin not only regarding body weight reduction but also regarding the decrease in fasting plasma glucose level, and fasting plasma insulin level. These results are consistent with those of previous reports that SGLT2 inhibitors ameliorate hepatic steatosis and improve insulin sensitivity. ^[22] Both hepatic steatosis and insulin resistance are known risk factors of cardiovascular disease. ^[23] Taken together, these data suggest that dapagliflozin might indeed be superior to sitagliptin for the cardiometabolic effects. In addition, previous studies reported the preferable cardiometabolic effects regarding SGLT2 inhibitors. ^[24]

In our study the changes in glycemic and metabolic parameters from baseline to week 12 in the two study groups. The mean ± SD change in Fasting plasma insulin from baseline to 12 weeks were 2.08 ± 0.76 and 2.37 ± 0.44 in the dapagliflozin and sitagliptin groups, respectively. The mean ± SD change in Fasting proinsulin/insulin ratio from baseline to 12 weeks were 1.04 ± 0.27 and 1.88 ± 0.23 in the dapagliflozin and sitagliptin groups, respectively. Some studies have directly compared the effects of DPP-4 and SGLT2 inhibitors. ^[25] DPP-4 inhibitors were found to be superior to SGLT2 inhibitors in glycaemic control, although different baseline characteristics were evaluated in these studies. Furthermore, SGLT2 inhibitors were found to have greater effects than DPP-4 inhibitors, regardless of metabolism and insulin resistance. However, these studies focused on the effects of glycaemic control, reduced body weight, and improved insulin resistance. ^[26]

In conclusion, to the best of our knowledge, this is the first study to directly compare the effects of sitagliptin and dapagliflozin on glycaemic control using Glucose stratified by BMI in Indian participants with early-stage type 2 diabetes. The response after 24 weeks of treatment was not significantly different between the sitagliptin and dapagliflozin groups. However, in the lower BMI subgroup, sitagliptin demonstrated superior efficacy in improving glycaemic variability than did dapagliflozin. Finally, our results demonstrated that, for the treatment of Indian patients with early-stage type 2 diabetes, patients should be stratified according to baseline BMI when selecting between sitagliptin and dapagliflozin.

Although dapagliflozin and sitagliptin provided similar effects on glycemic control with avoidance of hypoglycemic episodes, adequate loss in body weight occurred significantly more frequently in the dapagliflozin group. Additionally, various cardiometabolic indices improved to a significantly greater extent in the dapagliflozin group than in the sitagliptin group. Taken together, these data suggest that dapagliflozin therapy may be more effective for primary prevention of cardiometabolic risk factors in overweight patients with early-stage type 2 diabetes. Our findings are potentially useful in establishing an effective treatment strategy for patients with early-stage type 2 diabetes.

1. Pilotto A, Sancarlo D, Addante F, Scarcelli C, Franceschi M. Nonsteroidal anti-inflammatory drug use in the elderly. Surgical Oncology. 2010, 19: 167-172. 3.
2. Pulok K Mukherjee, Peter J Houghton. Evaluation of Herbal Medicinal Products, (Pharmaceutical press). 2009, 13-22.
3. Huerta C, Castellsague J, Varas-Lorenzo C and García Rodríguez LA. Nonsteroidal Anti-Inflammatory Drugs and Risk of ARF in the General Population. Am J Kidney Dis. 2002, 45(3): 531-539.
4. Tiwari, A.K. and M. Roa,. Diabetes mellitus and multiple therapeutic approaches of phytochemicals: Present status and future prospects. Curr. Sci.2002, 83: 30-38.
5. Atkinson, M.A. and N.K. Maclaren. The pathogenesis of insulin-dependent diabetes mellitus (review). N. Eng. J. Med.1994, 331: 1428-1436
6. Weber, C. and Noels, H. . Atherosclerosis: current pathogenesis and therapeutic options. Nature Medicine, 2011:17(11): 1410-1422.
7.  Raja, C., Devender, P. and Dharam, P.J.  “Antihyperlipidemic agents- a review.” Indian Drugs, 1996:33 (3): 85-97.
8. L.L. De Zwart, J.H. Meerman, J.N.M. Commandeur, N.P.E. Vermeulen, Biomarkers of free radical damage applications in experimental animals and in humans. Free Radical Biology and Medicine, 1999:26:202–226.
9. Harnack LJ, Rydell SA, Stang J, Prevalence of use of herbal products by adults in the Minneapolis/St Paul, Minn, metropolitan area. Mayo Clinic Proceedings,2001: 76: 688–694.
10. Athar,M.,Hussain S., Hussain,N.Drug metabolizing enzymes in liver.In:Kana SUS, Taketa, K,editors.Liver and Enveronmental Xenbiotics.New Delhi.Narosa publishing house 1997.
11. Baldessarini RJ, Hardman JG, Limbird LE, Gilman AG. Goodman & Gilman the Pharmacological Basis of Therapeutics. 10th ed. McGraw-Hill. 2001:472-473.
12. Nimal J, Babu CS, Harisudhan T, Ramanathan M. Evaluation of behavioral and anti oxidant activity of Cytisus scoparius Link in rats exposed to chronic unpredictable mild stress . BMC Complement Alter Med. 2008;8:15.
13. Ganguly, S. Indian ayurvedic and traditional medicinal implications of indigenously available plants, herbs and fruits: A review. Int. J. Res. Ayurveda Pharm. 2013, 4, 623–625.