ABSTRACT
Background:
Uncontrolled diabetes persists despite guideline-based treatment, partly attributed to inadequate patient involvement. This research addresses shared decision-making by eliciting patient preferences in Type 2 Diabetes Mellitus (T2DM) treatment based on certain key attributes and explores their correlation with socio-demographic-clinical profiles.
Methods:
A discrete choice experiment (DCE) was conducted among T2DM outpatients in an Indian tertiary care center. A choice card was developed using the contextual choice framework, having six second-line antidiabetic drugs (ADs) from different classes incorporating seven attributes. Face-to-face interviews were conducted with patients, and elicited preferences were analyzed using descriptive statistics, Chi-square analysis, and multinomial logistic regression.
Results:
Out of the 87 evaluated participant choices, the most preferred drug was Glimepiride (51.7%), followed by Dapagliflozin (22.9%) and Teneligliptin (17.2%). Overall, the most important attributes were the effect on weight (29%), followed by route of administration (24%), and additional benefits offered by the drug (18%). Significant associations were found between participants’ drug preferences and their age (P = 0.002), socioeconomic status (P = 0.04), occupation (P = 0.004), and monthly income (P = 0.03). Age was not a significant predictor of drug choice for any of the drugs. Multinomial logistic regression showed that the overall model was statistically significant (P = 0.025), and it correctly predicted drug choice for 58.6% of the participants.
Conclusion:
Glimepiride was the most preferred option overall while the effect on weight was the most important attribute for patients in determining their preference. The study highlighted the importance of shared decisions and can guide practitioners in considering patient preferences when prescribing antidiabetic drugs.
Keywords: Antidiabetic drugs, choice card, diabetes mellitus, patient preferences, shared decision-making
Introduction
Diabetes mellitus is a persistent metabolic ailment characterized by an excessive elevation of blood glucose levels, leading to an array of debilitating multi-system diseases. An estimated 77 million adults are suffering from Type 2 Diabetes Mellitus (T2DM) in India, making it the second most affected country in the world.[1]
Despite the existence of comprehensive guidelines, which provide pharmacological treatment algorithms in addition to lifestyle modifications,[2,3,4] the rate of uncontrolled diabetes among treated patients remains unacceptably high.[5,6] One of the key contributing factors to this issue is the inadequate involvement of patients in treatment decision-making, particularly in the Indian context.[7] The selection of add-on treatment for T2DM after the prescription of metformin is often based on the physician’s discretion rather than considering the patient’s preferences and priorities.
To address this, the concept of “Shared Decision-Making” has gained prominence. This approach involves a collaborative dialogue between the doctor and patient to create a joint treatment plan that incorporates both the medical expertise of the physician and the values, goals, and priorities of the patient. The result of this process is a treatment plan that is expected to be satisfactory to both parties.[8] The latest guidelines from the American Diabetes Association (ADA) 2023[3] and the Indian Council of Medical Research (ICMR) 2018 for the management of T2DM,[4] both stress the importance of ‘Shared Decision Making’.
One method to evaluate the patient’s role in shared decision-making is through the use of a discrete choice experiment (DCE). This attribute-based measure is based on the premise that healthcare interventions can be described by their characteristics, and an individual’s preference depends on the levels of these characteristics.[9] DCEs have been successfully conducted in international settings across various healthcare contexts and interventions,(9) and are useful in determining patient preferences for anti-diabetic drug attributes such as glucose-lowering efficacy, administration route, cost, and weight change.[10,11,12,13] However, there have been hardly any studies conducted in India utilizing this approach.
Understanding patient preferences through DCE can significantly benefit primary care physicians in India. By incorporating patient-centered approaches, physicians can align treatment plans more closely with patient priorities and values, leading to improved adherence and better treatment outcomes. This study aims to contribute valuable insights into patient preferences for antihyperglycemic agents (AHAs) prescribed after metformin, thereby facilitating informed shared decision-making and enhancing the quality of diabetes care in primary care settings.
Methods
This was an observational study. Convenience sampling was used to recruit eligible participants from the outpatient department and telemedicine facility of the department of medicine of a tertiary care hospital in central India.[14] The study was conducted between July 2022 and October 2022, following the Good Clinical Research Practice guidelines and obtaining ethical clearance from the Institutional Ethics Committee.[15] (IHEC-PGR/2022/STS-ICMR/7)
Inclusion criteria involved willing adults (18–80 years of age) of either sex, diagnosed with T2DM who were on any allopathic antidiabetic drug therapy, including at least metformin. Exclusion criteria included individuals unwilling to provide informed consent or those with known psychiatric/neurological conditions affecting cognition and impairing their ability to comprehend study-related information.
To ensure the reliability and validity of the survey, the DCE choice cards were administered via face-to-face interviews by a single study investigator. Basic socio-economic, demographic, and medical details were collected to ensure the representativeness of the sample population and the accuracy of analysis (survey form provided in the supplementary material (547KB, tif) ). After administering the survey form, patients were asked to select their most preferred drug from the choice card, and the attribute for their choice was also noted. The study methodology flowchart is depicted in Figure 1.
Figure 1.
Study Methodology Flowchart. *OPD = Out Patient Department
Development of the DCE choice card
The following drugs representing various classes of AHAs—Glimepiride (sulphonylureas), Pioglitazone (thiazolidinediones), Teneligliptin (DPP-4 inhibitors), Dapagliflozin (SGLT-2 inhibitors), Liraglutide (GLP-1 agonists), and Glargine (long-acting insulin). The attributes were included in the choice card after a thorough literature review, deliberation with experts, finalizing attributes using the Contextual Choice Framework,[16] and finally validating the construct, content, and face validity through expert evaluation and pilot testing (further details in the discussion section). These were cost (lowest dose of the drug), effect on weight, route of administration, frequency of administration, % reduction in HbA1C, additional benefits, and adverse effects of the drugs. Levels and values were assigned to these attributes for the different drugs using published evidence through guidelines, drug monographs, and articles.[2,3,9,17,18,19,20,21,22,23] The choice cards were made available in both Hindi and English (provided in the supplementary material (547KB, tif) ).
Data analysis
The data obtained from the DCE survey was then analyzed using the IBM SPSS Statistics for Windows version 25.0 (Armonk, NY: IBM Corp.) A comparison of the participants’ socioeconomic data (according to the modified Kuppuswamy scale), clinical and demographic details, and their most preferred choice of drug were done. Chi-square analysis was done to determine any relationship between various independent variables and their preferred choice of drug.[24] A multinomial logistic regression analysis was further conducted to examine the relationship between the significant variables (as established by the Chi-square tests) and the most preferred drug choices.[25]
Results
Participant profile
A total of 125 patients were approached for carrying out the Discrete Choice Experiment, out of which choices were elicited from a total of 87 patients after obtaining their consent. (69.6% participation rate). Among the responses of 87 participants who were available for analysis, 55.2% were males. The average age of participants was 57.2 ± 11.4 years, the average duration since diagnosis of diabetes was 11.3 ± 5.6 years, while 64.4% (n = 56) of them had comorbidities (hypertension, dyslipidemia, etc.) along with diabetes. Further demographic and clinical characteristics of the participants are presented in Table 1.
Table 1.
Socio-demographic characteristics of participants
| Characteristics | n (%) |
|---|---|
| Gender (n=87) | |
| Male | 48 (55.2) |
| Female | 39 (44.8) |
| Age (in years) (n=87) | |
| Mean | 57.2±11.4 |
| BMI* (in kg/m2) (n=63) | |
| Mean | 25.6±3.9 |
| Normal | 34 (53.4) |
| Overweight/Obese | 29 (46.6) |
| HbA1C (n=65) | |
| Controlled (<7%) | 27 (41.5) |
| Uncontrolled (>7%) | 38 (58.5) |
| Education (n=65) | |
| Middle school | 3 (4.6) |
| High school | 13 (20) |
| Intermediate/Diploma | 32 (49.2) |
| Graduate | 17 (26.1) |
| Occupation (n=65) | |
| Semiskilled Worker | 3 (4.6) |
| Skilled Worker | 9 (13.8) |
| Clerical/Shop/Farm | 11 (16.9) |
| Semi-professional | 34 (52.3) |
| Professional | 5 (7.6) |
| Semiskilled Worker | 3 (4.6) |
| Monthly Income (n=65) (Based on the Modified Kuppuswamy scale) | |
| INR 6,175–18,496 | 20 (30.7) |
| INR 18,497–30,830 | 17 (26.1) |
| INR 30,831–46,128 | 15 (23.1) |
| INR 46,129–61,662 | 9 (13.8) |
| INR 61,663–1,23,321 | 3 (4.6) |
| >INR 1,23,322 | 3 (4.6) |
| Socioeconomic Status (n=65) | |
| Upper | 3 (4.6) |
| Upper Middle | 24 (36.9) |
| Lower Middle | 28 (43.1) |
| Upper Lower | 10 (15.4) |
| Average time since diagnosis of T2DM (in years) (n=62) | 11.3±5.6 |
| Comorbidities (HTN, dyslipidemia) (n=56) | |
| With | 36 (64.4) |
| Without | 20 (35.6) |
| Complications (n=56) | |
| With | 31 (55.4) |
| Without | 25 (44.6) |
*BMI=Body Mass Index
Drug preferences and attributes cited in their favor
As shown in Table 2, we found that the drug most preferred by participants in our setting was Glimepiride (52%), followed by Dapagliflozin (23%) and Teneligliptin (17%).
Table 2.
Drug preferences of participants and common attributes for choosing each drug
| Most preferred drug | Number (%) of participants | Attributes in favor (n, %) | ||
|---|---|---|---|---|
| Glimepiride | 45 (51.7) | Route of administration (17, 37.7) | Maximum Potential (11, 24.4) | Cost (6, 13.3) |
| Dapagliflozin | 20 (22.9) | Effect on weight (17,85) | Additional Benefits (2,10) | Maximum Potential (1,5) |
| Teneligliptin | 15 (17.2) | Additional Benefits (12, 80) | Route of administration (2, 13.3) | Effect on weight (1, 6.6) |
| Liraglutide | 4 (4.6) | Effect on weight (2,50) | Additional Benefits (2,50) | |
| Pioglitazone | 2 (2.3) | Route of administration (1, 100) | ||
| Glargine | 1 (1.5) | Maximum potential (1, 100) | ||
The most cited attributes in favor of choosing a particular drug are also shown in Table 2. Overall, it was found that the most important attributes as ascertained by the participants for choosing any drug were the effect on weight (29%), followed by route of administration (23%), and additional benefits offered by the drug (18%). Drug-wise attributes for each drug are listed in Table 2 below.
Relating participants’ preferences with their sociodemographic and clinical characteristics
Analysis of participants’ characteristics in choosing the three most preferred drugs (Glimepiride, Dapagliflozin, and Teneligliptin) was carried out and is shown below in Table 3.
Table 3.
Preferred drug-wise attributes of participants
| Parameter | Preferred Drug | ||
|---|---|---|---|
|
| |||
| Glimepiride | Teneligliptin | Dapagliflozin | |
| Gender | |||
| Male (n=44) | 68.2% | 13.6% | 18.2% |
| Female (n=36) | 41.7% | 25% | 33.3% |
| AGE (in years) | |||
| Mean | 64.86±11.6 | 55.8±11.9 | 53.4±11.4 |
| 30–39 (n=4) | 25% | 0% | 75% |
| 40–49 (n=21) | 38.1% | 33.3% | 28.6% |
| 50–59 (n=14) | 71.4% | 7.1% | 21.4% |
| 60–69 (n=25) | 76% | 24% | 0% |
| >70 (n=9) | 33.3% | 11.1% | 55.6% |
| BMI (in kg/m2) | |||
| Mean | 25.2±3.6 | 26.3±2.5 | 26.3±5.4 |
| Normal (18.5–24.9) (n=31) | 58.1% | 9.7% | 32.3% |
| Overweight (25–29.9) (n=19) | 73.7% | 21.1% | 5.3% |
| Obese (≥30) (n=7) | 42.9% | 14.3% | 42.9% |
| Blood sugar control (HbA1C) | |||
| Controlled (<7%) (n=27) | 51.9% | 25.9% | 22.2% |
| Uncontrolled (≥7%) (n=38) | 57.9% | 15.8% | 26.3% |
| Education | |||
| Middle school (n=3) | 100% | 0% | 0% |
| High school (n=10) | 40% | 10% | 50% |
| Intermediate/Diploma (n=29) | 69% | 13.8% | 17.2% |
| Graduate (n=17) | 58.8% | 17.6% | 23.5% |
| Occupation | |||
| Semiskilled Worker (n=3) | 100% | 0% | 0% |
| Skilled Worker (n=9) | 33.3% | 44.4% | 22.2% |
| Clerical/Shop/Farm (n=9) | 88.9% | 11.1% | 0% |
| Semi-professional (n=32) | 68.7% | 9.4% | 21.9% |
| Professional (n=5) | 20.0% | 0% | 80% |
| Monthly income (according to Modified Kuppuswamy scale) | |||
| INR 6,175–18,496 (n=20) | 65% | 10% | 25% |
| INR 18,497–30,830 (n=14) | 64.3% | 35.7% | 0% |
| INR 30,831–46,128 (n=12) | 50% | 0% | 50% |
| INR 46,129–61,662 (n=6) | 50% | 0% | 50% |
| INR 61,663–1,23,321 (n=3) | 83.3% | 0% | 16.7% |
| >INR 1,23,322 (n=3) | 66.7% | 33.3% | 0% |
| Socioeconomic status | |||
| Upper (n=3) | 33.3% | 0% | 66.7% |
| Upper Middle (n=24) | 58.3% | 8.3% | 33.3% |
| Lower Middle (n=25) | 72% | 24% | 4% |
| Upper Lower (n=7) | 57.1% | 0% | 42.9% |
| Route of administration of prescribed drugs in participants | |||
| On oral pills only (n=55) | 61.8% | 18.2% | 20% |
| On oral as well as injectable medications (n=12) | 33.3% | 25% | 41.7% |
On comparing the proportions for different parameters for the drug preference among the participants, no significant association was found for BMI, education level, blood sugar control, their route of taking their currently prescribed medications (oral or injectable), whether they did or did not have comorbidities like hypertension, dyslipidemia or whether they had or had not developed complications.
Significant associations were found between the participants’ preference for drugs and their age [χ²(8) = 24.044, P = 0.002], socioeconomic status (according to modified Kuppuswamy scale) [χ²(6) = 13.158, P = 0.04], occupation [χ²(8) = 22.265, P = 0.004], and monthly income [χ²(10) = 19.801, P = 0.03].
Multinomial regression analysis of significantly associated characteristics
A multinomial logistic regression was further conducted to predict a model including age and socioeconomic status as the predictor variables and the three main drug choices, namely, Glimepiride, Dapagliflozin, and Teneligliptin as the outcome variable. Since the participants’ monthly income and occupation impact their socioeconomic status, we did not include these variables separately. Only those observations were included in this analysis for which there were no missing values in age or socioeconomic status of the individual (n = 58).
The final model showed overall statistical significance (P = 0.025). The Cox and Snell, Nagelkerke, and McFadden pseudo R-squares were 0.261, 0.311, and 0.166, respectively. R-square values range from 0 to 1, 1 indicating a perfect model. Putting the context of our study and the sample size in perspective, the aforementioned values indicate a fairly adequate fit.[26,27,28] The results showed that compared to the Teneligliptin, Glimepiride had significantly lower odds of being chosen when the participant belonged to the upper socioeconomic class (odds ratio [OR] = 0.357, P < 0.001), upper middle class (OR = 0.129, P < 0.001), or lower middle class (OR = 0.524, P < 0.001). Dapagliflozin had significantly higher odds of being chosen over teneligliptin when the participant belonged to the upper socioeconomic class (OR = 1.985, P < 0.001), upper middle class (OR = 1.508, P < 0.001), or lower middle class (OR = 6.002, P < 0.001). Age was not a significant predictor of drug choice for any of the drugs. It was also seen that the model correctly predicted drug choice for 58.6% of the participants.
Discussion
A DCE is a research method used to understand how people make choices when faced with different options. DCEs are commonly used in healthcare to inform decision-making by providing insights into patient preferences and helping to guide treatment choices by often using the modality of a “choice card” that asks participants to choose from presented options and provide reasons for the same.[29]
Developing the choice card
First, we identified the antidiabetic medications (AHAs) to be included in the experiment by using a two-step approach. Firstly, a review of published literature was conducted to identify the most commonly prescribed drugs in India after metformin for the management of T2DM.[30] Secondly, expert physicians at our tertiary care hospital where the study was conducted were consulted to finalize the selection of the top six drugs commonly prescribed in our study setting.
Next, a literature review was conducted to identify commonly used attributes and levels in DCE studies conducted for anti-diabetic medications worldwide.[9,10,11,12,13,19,31,32,33,34] The Contextual Choice Framework (CCF) was applied to identify attributes and levels that were most relevant to the Indian context. Expert physicians were consulted to validate and refine the identified attributes and levels. We finalized it to seven attributes and their appropriate levels and prepared the choice card in both Hindi and English.
The rationale for choosing six drugs and eight attributes was to ensure sufficient statistical power and respondent engagement while ensuring that respondents do not experience excessive fatigue or difficulty in making choices.[29] The levels of various attributes were appropriately pictorially depicted as well to aid the relatively less literate participant population. Additionally, we adopted unlabeled DCE; our choice card did not reveal the specific brand or drug names being evaluated, which helped reduce bias and increased the generalizability of the study results. The order of presentation of the drugs on the choice card was randomized to minimize any order bias.
To ensure the choice card was easily comprehensible to patients, a pilot study was conducted. The pilot study involved a sample of diabetic patients from the same hospital where the main study was conducted. Based on feedback from the pilot study, minor modifications were made to the choice card.
Qualitative report of conducting the DCE
To our knowledge, very few discrete choice experiments have been carried out in India for healthcare purposes, none of them being for patients with diabetes mellitus.[35,36,37,38,39,40] Given the lack of space and time during the OPD rush hours, this was a challenging experiment to carry out in Indian settings. For the 37 patients from whom we could not elicit a choice despite approaching, the reasons were multifold, such as failing to understand the purpose of the experiment, trouble in understanding the choice card and information contained or the explanation of the investigator, or simply unwillingness to participate in the study. The patients generally expressed difficulty and apprehension in choosing a drug on their own rather than the drug being chosen by the physician. This exemplifies how unaware or inactive patients are in regards to their own treatments. To overcome this, the patients were subsequently assured that their choices were only being noted for research purpose, and that their treatment would be taken care of the physician only (as per routine practice). The concept of ‘shared decision-making’ was alien to the majority of the patient population which is by the findings of certain studies.[7] The time taken to explain the choice card of DCE, the lack of patients’ knowledge regarding the drugs, and the lack in acknowledging their role in their treatment plan were key barriers to the conduction of the study.
Understanding and interpreting the results
In our study, we found that the foremost key attributes preferred by patients in these settings were the effect of drugs on weight—those causing weight loss (Dapagliflozin, Liraglutide) and weight neutral (Teneligliptin) were found more appealing. There was a considerable propensity to lean towards the oral route of administration rather than injectables. Additional benefits of the drug, which though not a routinely considered attribute in DCEs, was found to garner attention from the patients as well—Dapagliflozin has been shown to have positive effects on cardiac health—a point which was appealing to a considerable number of patients. The cost attribute was also a crucial deciding factor for preferring the most commonly chosen drug—Glimepiride, given the lower cost of the drug compared to the other alternatives. This makes sense given that the pill burden and cost of therapy remain high among these patients with a significant contribution of comorbidities— the median monthly cost of drug therapy with branded prescribing was found to be INR 870.43 and INR 393.72 with the use of generics in a previous study in the institute.[41] Diabetes Mellitus frequently coexists with comorbidities such as hypertension and dyslipidemia, necessitating concurrent therapeutic interventions culminating in an increased pill count. Likewise, participants suffering from complications like neuropathy, nephropathy, etc., also have increased pill count. Notably, Dapagliflozin has demonstrated efficacy in reducing blood pressure levels, rendering it an appealing option for hypertensive individuals.[42] These dual dynamics possess the potential to intricately shape the distinct drug preferences of the participants suffering from comorbidities and complications. In our current investigation, our findings do not substantiate a significant correlation between the prevalent comorbidities, patient complications, and their corresponding medication preferences. It is imperative, however, to acknowledge the constraints imposed by our limited sample size, which could underlie the absence of statistically significant associations. Consequently, the pursuit of comprehensive insights demands the undertaking of subsequent studies to thoroughly interrogate this nuanced relationship.
Comparison with other studies
We found that the attribute given the least priority was the frequency of administration. While this is in line with a study conducted in China,[34] most of our results are different from several previous studies. For example, a study found that diabetes patients in Germany and Spain were willing to trade efficacy for improvements in side effects.[32] Another study found that key determinants of treatment preferences among diabetes patients in Germany and the United Kingdom were side effects, efficacy, and dosing frequency,[43] which is considerably different from our results. Studies have also found that the majority of patients preferred glucose control over avoiding minor hypoglycaemic events. The ability of a drug to lower blood glucose levels was found to play a decisive role in the choice between alternative treatments.[10,11,12,13] Previous DCEs on diabetes patients’ preferences mainly focused on therapeutic interventions in clinical settings, and several preferable attributes of anti-diabetic drugs were identified, such as the chance of reaching the target glycated hemoglobin (HbA1c) level,[31,32] risk of hypoglycemia,[31] risk of gastrointestinal problems,[32] route of drug administration,[33] out-of-pocket costs, and life expectancy.[10] These results are different from our study, which indicates that the effect on weight change, route of administration of the drug, additional benefits profile, and drug cost are the key attributes in determining patients’ preference for a particular drug in tertiary-level public care settings in India.
Though there have been multiple studies conducted internationally for eliciting the choices among attributes of the drugs in different therapeutic domains, there are no DCEs that have elicited the choice of antidiabetic drugs. In our study we found that the most chosen drug by the patients was Glimepiride, which is also the most common add on drug after metformin in these settings.[30] Studies suggest that Dipeptidyl peptidase-4 inhibitors seem to be fast catching up with sulfonylureas as a second-line treatment after metformin.[30] Our study puts dapagliflozin (SGLT-2 Inhibitors) as the second and teneligliptin (DPP-4 inhibitor) as the third most chosen drug, which can serve as templates for prescribing them more frequently in such settings.
Strengths and limitations
The major strengths of our study lie firstly in the use of the unique DCE methodology allowing for the quantification of the strength of preferences and identification of preference heterogeneity. Second, we involved attributes from both clinical benefits and convenience perspectives. Our findings would be helpful for the healthcare provider’s better understanding of the multi-attribute value of existing antidiabetic drugs and can provide the same for the newer drugs to come. Third, we captured preference heterogeneity evidence to help physicians make prescription decisions more patient-centered in such settings. Fourth, the preferences of our population have greater credibility since most participants had over a decade of experience with antidiabetic medications.
Despite these strengths, a few limitations of our study need to be acknowledged. We only selected a subset of prominent attributes that were identified from the literature review. Our analysis did not address other attributes that might be meaningful to patients. At times, the choice decisions were made by the educated relatives of the patients rather than the patients themselves. Thus, there may be a difference between the actual choice of the patient and the perceived choice of the relative. We employed a convenience sampling strategy to assemble our participant cohort, a choice motivated by practical constraints including temporal restrictions and personnel availability. Consequently, the resultant sample size remained modest in scale. This circumstance gave rise to instances of subgroups within which the sample size ended up being less than 5 in our diverse subgroup analyses, like the participants aligned with the upper socioeconomic stratum and certain age groups. But this is also driven in part by patient preferences. Regrettably, this methodology, albeit expedient, exposes our study to potential sampling bias, a factor that can subsequently exert an impact on the ultimate statistical outcomes derived. Due to constrained logistics and limited time, our study population was small and provides evidence of patients’ preferences at only one tertiary care center in India; more such studies with larger sample sizes are required to further validate our results. As discussed before, findings related to preferences have shown substantial variability when seen in the context of different countries. The same variations may be expected if this kind of experiment is conducted in different regions of India as well or at different levels of care. Finally, DCEs pose hypothetical choices, which may not fully represent the choices respondents have or would make in real-world decision scenarios.
The general impact and insights gained
The general sentiment of patients for DCE indicated that the experiment provided the patients not only information regarding the management of diabetes but also made them understand the importance of “shared decision-making,” generating a positive attitude towards the medical staff involved and a greater gravity in their resolve to maintain adherence to their lifestyle modifications and medications for the management of T2DM.
DCE has rarely been carried out in the Indian healthcare setting despite its proven utility. This experiment can be carried out more extensively if dedicated time, space, and efforts are put forth for patient education and conduction of such modalities.
Conclusion
In the DCE, the most preferred option chosen by participants was glimepiride overall and across different subgroups as well, and dapagliflozin and teneligliptin were the other most commonly chosen options with certain subgroups preferring one over the other. The most important attribute for patients was the effect on weight, with weight loss causing drugs being preferred, followed by the route of administration and additional benefits like cardiac or renal safety or improvement, which should be considered and given greater importance when deciding the treatment for patients.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgements
We express gratitude to Dr. Sayan K. Das, Dr. Kenam Shah, Shrenik Vaidya, Khushi Meghani, and Kanishka Tenguriya for their technical assistance in carrying out the study.
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