Abstract
Background:
Prevalence of diabetes is on a rising trend worldwide as well as in India. Diabetes control remains suboptimal in India’s noncommunicable diseases (NCD) clinics despite the implementation of the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD). This study assessed glycemic status and determinants among diabetics attending health and wellness center (HWCs)–urban primary health centers (UPHCs).
Materials and Methods:
Cross-sectional study of 164 type 2 diabetics (≥6 months duration) consecutively recruited from NCD clinics of two UPHCs in the months of October and November 2025. Glycemic status was determined using hemoglobin A1c (HbA1c) where available, or fasting and postprandial blood glucose values according to the American Diabetes Association criteria. Medication adherence was measured using the Hill–Bone Medication Adherence Scale and blood investigation compliance was measured objectively. Multivariable logistic regression identified determinants of uncontrolled glycemic status, and a sensitivity analysis restricted to participants with HbA1c measurements was performed.
Results:
The proportion of diabetics with uncontrolled glycemic status was 59.8% (95% confidence interval [CI]: 51.8–67.3). Significant predictors included male gender (adjusted odds ratio [aOR]: 2.59; 95% CI: 1.11–6.05; P = 0.027), obesity (aOR: 0.31; 95% CI: 0.13–0.74; P = 0.008), diabetes duration >5 years (aOR: 4.55; 95% CI: 2.02–10.21; P < 0.001), and blood investigation non-compliance (aOR: 8.47; 95% CI: 3.54–20.28; P < 0.001). Sensitivity analysis yielded findings consistent with the primary analysis. Major barriers to blood investigation non-compliance were inconvenience, waiting time, and forgetfulness.
Conclusion:
High uncontrolled glycemia (59.8%) among NCD clinic attendees underscores implementation gaps. Male gender, long duration, and investigation non-compliance drive poor outcomes. Point-of-care testing and targeted interventions can optimize primary care diabetes management under the NP-NCD.
Keywords: Diabetes mellitus, glycemic control, medication adherence, noncommunicable diseases, patient compliance, primary health care
BACKGROUND
An estimated 589 million adults aged 20–79 years were living with diabetes worldwide in 2024, corresponding to a global prevalence of 11.1%.[1,2] This number is projected to rise to 853 million by 2050.[1,3] Type 2 diabetes accounts for the vast majority of these cases and is a major contributor to cardiovascular disease, chronic kidney disease, blindness, and premature mortality.[1,2,3] India has the second highest number of adults with diabetes in the world, with about 89.8 million adults (20–79 years) estimated to be living with the condition in 2024 and an adult prevalence of around 10.5%.[1,4] Projections suggest that the number of adults with diabetes in India will increase to around 156.7 million by 2050, underscoring the scale of the challenge for the health system.[4]
The high and rising prevalence is accompanied by a substantial proportion of undiagnosed cases and suboptimal glycemic control among those on treatment, which together drive high rates of complications and healthcare costs.[1,2] Persistent hyperglycemia is associated with accelerated progression of microvascular complications such as diabetic retinopathy, nephropathy, and neuropathy, and macrovascular complications, including coronary artery disease, stroke, and peripheral arterial disease.[5,6,7] Large randomized trials have demonstrated that maintaining optimal glycemic control significantly reduces the risk of these complications and improves long-term quality of life.[5,6] However, achieving recommended glycemic targets remains difficult in routine clinical practice because of delayed diagnosis, therapeutic inertia, medication non-adherence, inadequate lifestyle modification, irregular follow-up, and limited access to laboratory monitoring.[8,9,10] These challenges are particularly pronounced in low- and middle-income countries, where health-system constraints further compromise diabetes care.[11]
In India, several studies have demonstrated that glycemic control among people living with diabetes remains suboptimal despite increasing availability of treatment services. The National Noncommunicable Diseases (NCD) Monitoring Survey reported that only a minority of patients receiving treatment achieved adequate glycemic control.[12] Similarly, the study by Borgharkar et al and other multicentric Indian studies have reported that 40%–70% of adults with type 2 diabetes have hemoglobin A1c (HbA1c) levels above recommended targets, indicating substantial gaps in disease management.[9,10] Multiple factors contribute to poor glycemic control, including longer duration of diabetes, obesity, medication non-adherence, therapeutic inertia, socioeconomic disparities, inadequate patient education, and irregular monitoring.[8,9,10,13] Understanding these determinants in different healthcare settings is essential for designing interventions tailored to local contexts.
To address the growing burden of diabetes and other chronic diseases, the Government of India has implemented the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD), which builds on and expands the earlier National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke framework.[14,15] Under NP-NCD, Ayushman Arogya Mandirs (sub-centers and primary health centers upgraded as HWCs) are mandated to provide a package of services that includes population-based and opportunistic screening of adults aged 30 years and above for diabetes and hypertension, confirmation of diagnosis, initiation and continuation of treatment, counseling on healthy lifestyle practices, and periodic follow-up using standardized treatment protocols.[14,15] Regular blood glucose monitoring and assessment of treatment adherence constitute important components of diabetes care under the program.
Regular assessment of glycemic status is fundamental to diabetes management because it guides therapeutic decisions, facilitates timely treatment intensification, and enables early identification of patients at risk of developing complications.[7] Glycated hemoglobin (HbA1c) reflects average blood glucose levels over the preceding 2 to 3 months and is considered the preferred indicator of long-term glycemic control, whereas fasting blood glucose (FBS) and postprandial blood glucose (PPBS) provide information on short-term glycemic status and day-to-day glucose variability.[7] International guidelines recommend periodic monitoring using these measures to assess treatment response, guide treatment decisions, and achieve individualized glycemic targets.[7] However, in routine primary care practice, particularly in resource-constrained settings, access to HbA1c testing remains limited because of cost, laboratory infrastructure, and logistical constraints. Consequently, many health facilities continue to rely predominantly on FBS and PPBS measurements. Although these tests are widely available and inexpensive, they require patients to attend the health facility in the fasting state, consume a meal or glucose load, and remain for approximately two hours before the postprandial sample can be collected. These requirements increase patient inconvenience, travel costs, loss of working hours, waiting time, and dependence on laboratory services, which may adversely affect compliance with recommended blood investigations and delay timely treatment decisions.[16,17]
Though the NP-NCD emphasizes continuity of care through monthly NCD clinics, standardized treatment protocols, free essential medicines, patient counseling, and periodic laboratory investigations to support long-term disease control, several operational challenges continue to affect program implementation. Although several Indian studies have evaluated glycemic control among people with type 2 diabetes, most have been conducted in tertiary care hospitals, private diabetes clinics, or specialist endocrine centers. Evidence from health and wellness center (HWC)–urban primary health centers (UPHCs) implementing the NP-NCD is relatively limited, particularly regarding operational determinants of glycemic control and patients’ adherence to recommended blood investigations. Understanding these determinants in the primary healthcare setting is important because most individuals with uncomplicated diabetes receive long-term follow-up through these facilities. Therefore, the present study was conducted among adults with type 2 diabetes attending NCD clinics of two HWC–UPHCs in Kalyani, West Bengal, to determine the proportion of patients with uncontrolled glycemic status, identify factors associated with poor glycemic control, and explore reasons for non-compliance with recommended blood investigations.
MATERIALS AND METHODS
We conducted a cross-sectional study among people living with type 2 diabetes mellitus attending the NCD clinic of the UPHCs of Kalyani, Nadia, West Bengal in the months of October and November 2025. There are two UPHCs in the Kalyani municipality area. Each of them caters to a population of about 60,000 and have about 150 patients attending the NCD clinics per month. These centers have facilities for FBS and PPBS estimation using glucose oxidase–peroxidase (GOD–POD) method and provide basic diabetic medications to NCD clinic patients following monthly follow-ups by a physician. The glycemic status of patients in one of the two HWCs was also assessed using point-of-care HbA1c measured by HemoCue® HbA1c 501 (HemoCue AB, Ängelholm, Sweden)[18] machine as a part of a different study. The machine has been validated for both diagnosis and follow-up of diabetes patients.[19,20]
The sample size was calculated to estimate the proportion of uncontrolled glycemic status among people living with type 2 diabetes. As no local estimates from similar urban primary care settings were available, the prevalence (p) was assumed to be 50%, which provides the maximum sample size. With a confidence level of 95% (Z = 1.96) and an absolute precision (d) of 8%, the required sample size was calculated using the formula:
n = Z²p (1 − p)/d²
Therefore, a minimum sample size of 151 participants was required. Finally, 164 eligible participants were enrolled.
All the registered NCD patients aged 18 years and above with or without comorbidities attending the NCD clinics of both UPHCs having a history of type 2 diabetes for at least 6 months and who provided consent were included in the study. All patients attending the NCD clinics during the study period were screened for inclusion criteria and consecutively recruited for the study.
A semi-structured questionnaire was administered by trained interviewers to collect sociodemographic data and data regarding type and frequency of blood investigations, comorbidity history, current medications, and other medical history. The self-reported data were also verified by their previous medical records (reports and prescription slips) where they were available. Height and weight were measured using standard methods. Those having a body mass index (BMI) <18.5 were considered underweight, 18.5–22.9 as normal, 23–24.9 as overweight, and ≥25 as obese.[21] Glycemic status was determined using laboratory reports obtained within the previous 3 months. HbA1c was considered the preferred indicator because it reflects average glycemia over approximately 3 months and is recommended for routine diabetes monitoring. When HbA1c was unavailable, FBS together with PPBS reports were used. Those with HbA1c <7%, FBS <130 mg/dL, and/or PPBS <180 mg/dL were considered under glycemic control.[7] Medication adherence was assessed using the modified Hill–Bone Medication Adherence scale[22] (originally for hypertension) after making modifications for diabetes as used in a few Indian studies.[23,24] The scale comprises nine items, each scored on a 4-point Likert scale (1–4), with a total score ranging from 9 to 36. Higher scores indicate better medication adherence. The scale was translated into Bengali using a standard forward–backward translation procedure. Initially, the original English version was independently translated into Bengali by a bilingual expert fluent in both English and Bengali. The Bengali version was then back-translated into English by a second independent bilingual expert who was blinded to the original version. The forward and backward translations were compared with the original scale, and discrepancies were resolved through discussion. Blood investigation compliance was assessed by reviewing laboratory reports and medical records for the preceding 6 months. Participants were classified as compliant if they had undergone the recommended blood investigations as advised by their treating physician during the preceding 6 months. Those who had not undergone the advised investigations at the recommended frequency were classified as non-compliant. Reasons for non-compliance with blood investigations were collected using open-ended questions and later grouped into categories.
The data were collected using physical forms, entered, cleaned using Microsoft Excel, and analyzed using STATA version 19 (StataCorp LLC, College Station, TX, USA). Categorical data were represented as frequencies and percentages. Continuous data were represented as mean ± standard deviation. Skewed data were represented as median and interquartile range (IQR). Bivariate logistic regression was done, and all variables, irrespective of P value, were included in the multivariable logistic regression model. A P value of 0.05 was considered significant. There were no missing data for the variables included in the primary multivariable logistic regression. To assess the robustness of the findings and evaluate the potential influence of using different measures of glycemic control, a sensitivity analysis was performed by restricting the multivariable logistic regression to participants with available HbA1c measurements (n = 94). The same independent variables included in the primary model were retained, and the direction and magnitude of the adjusted odds ratios were compared with those of the primary analysis.
Ethical approval was obtained from the Institutional Ethics Committee of All India Institute of Medical Sciences, Kalyani (Ref no.: IEC/AIIMS/Kalyani/certificate/2025/186).
RESULTS
A total of 229 patients attending the NCD clinics were screened for eligibility, of whom 65 were excluded [Figure 1]. Finally, 164 participants (82 from each HWC–UPHC) were enrolled in the study. The mean age was 56.2 ± 10.2 years, and 61.0% were females. The duration of diabetes ranged from 7 months to 30 years. About 45.7% were obese, 61.6% had hypertension, and 32.9% had at least one additional comorbidity. The median Hill–Bone medication adherence score was 34 (IQR: 33–36), while 53.7% of participants were non-compliant with recommended blood investigations [Table 1]. Ninety four of them had HbA1c reports, and the remaining had FBS and PPBS reports. The proportion of diabetic patients with uncontrolled glycemic status was 59.8% (95% CI: 51.8–67.3) [Table 1]. On bivariate logistic regression [Table 2], duration of diabetes more than 5 years (crude odds ratio [cOR]: 2.48; 95% confidence interval [CI]: 1.31–4.71; P = 0.005) and non-compliance to blood investigations (cOR: 4.13; 95% CI: 2.12–8.01; P < 0.001) were found to be significantly associated with uncontrolled glycemic status. All the variables were included in multivariable logistic regression [Table 2] and male gender (adjusted odds ratio [aOR]: 2.59; 95% CI: 1.11–6.05; P = 0.027), obesity (aOR: 0.31; 95% CI: 0.13–0.74; P = 0.008), duration of diabetes more than 5 years (aOR: 4.55; 95% CI: 2.02–10.21; P < 0.001) and non-compliance to blood investigations (aOR: 8.47; 95% CI: 3.54–20.28; P < 0.001) were found to be significantly associated with uncontrolled glycemic status. The model fit was good (Nagelkerke’s Pseudo R2 = 0.37).
Figure 1.
Flow diagram of participant recruitment and inclusion in the study
Table 1.
Sociodemographic data (n=164)
| Variable | Frequency (%)/Mean±SD | |||
|---|---|---|---|---|
| Age, years | 56.2±10.2 | |||
| Gender | Male | 64 (39.0) | ||
| female | 100 (61.0) | |||
| Duration of diabetes, years | ≤ 5 years | 80 (48.8) | ||
| > 5 years | 84 (51.2) | |||
| Antidiabetic treatment regimen | Mono drug OHA therapy | 15 | ||
| Dual drug OHA therapy | 102 | |||
| Triple drug OHA therapy | 26 | |||
| 4 drug OHA therapy | 15 | |||
| Insulin±OHA | 6 | |||
| BMI | 24.9±4.0 | |||
| Obesity status | Underweight | 3 (1.8) | ||
| Normal | 57 (34.8) | |||
| Overweight | 29 (17.7) | |||
| Obese | 75 (45.7) | |||
| Hypertension | Present | 101 (61.6) | ||
| Absent | 63 (38.4) | |||
| Other Comorbidities† | Present | 54 (32.9) | ||
| Absent | 110 (67.1) | |||
| Glycemic status | Controlled | 66 (40.2) | ||
| Uncontrolled‡ | 98 (59.8) | |||
| Blood investigations | Compliant | 76 (46.3) | ||
| Non-compliant | 88 (53.7) | |||
| Hill–Bone medication adherence scale* | 34 [33, 36] | |||
BMI=Body mass index, SD=Standard deviation, OHA=Oral hypoglycemic agent, IQR=Interquartile range, HbA1c=Glycated hemoglobin, FBS=Fasting blood sugar, PPBS=Postprandial blood sugar. * Median [IQR]. †Dyslipidemia, cerebrovascular disease, coronary artery disease, thyroid disorder, osteoarthritis, neuropathy, adenocarcinoma prostate. ‡Out of the 98 patients with uncontrolled glycemic status, 54 had raised HbA1c, 26 had raised FBS and PPBS, 5 had raised FBS alone, and 13 had raised PPBS alone
Table 2.
Bivariate and multivariable logistic regression for determinants of uncontrolled glycemic status (n=164)
| Variable | cOR (95% CI) | P | aOR (95% CI) | P | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age, years | 0.98 (0.95–1.01) | 0.130 | 0.96 (0.92–1.00) | 0.072 | ||||||
| Gender | Female | (ref) | (ref) | |||||||
| Male | 1.21 (0.63–2.30) | 0.567 | 2.59 (1.11–6.05) | 0.027* | ||||||
| BMI | <23 | (ref) | (ref) | |||||||
| 23–24.5 | 1.41 (0.53–3.74) | 0.485 | 1.30 (0.40–4.22) | 0.660 | ||||||
| ≥25 | 0.55 (0.28–1.11) | 0.096 | 0.31 (0.13–0.74) | 0.008* | ||||||
| Duration of diabetes | ≤ 5 years | (ref) | ||||||||
| > 5 years | 2.48 (1.31–4.71) | 0.005* | 4.55 (2.02–10.21) | <0.001* | ||||||
| Hypertension | Absent | (ref) | (ref) | |||||||
| Present | 0.62 (0.32–1.20) | 0.155 | 1.26 (0.54–2.95) | 0.594 | ||||||
| Other Comorbidities | Absent | (ref) | (ref) | |||||||
| Present | 0.55 (0.28–1.06) | 0.076 | 0.59 (0.26–1.35) | 0.213 | ||||||
| Blood investigations | Compliant | (ref) | (ref) | |||||||
| Non-compliant | 4.13 (2.12–8.01) | <0.001* | 8.47 (3.54–20.28) | <0.001* | ||||||
| Hill–Bone medication adherence scale (0–36) | 0.90 (0.80–1.01) | 0.072 | 0.89 (0.76–1.04) | 0.135 | ||||||
BMI=Body mass index, CI=Confidence interval, cOR=Crude odds ratio, aOR=Adjusted odds ratio. *P<0.05. †Nagelkerke’s Pseudo R2=0.37. ‡VIF=1.16
A sensitivity analysis restricted to participants with available HbA1c measurements (n = 94) yielded findings consistent with the primary analysis [Supplementary Table S1]. Diabetes duration greater than 5 years and blood investigation non-compliance remained independently associated with uncontrolled glycemic status, while the direction of association for male gender and obesity was unchanged despite wider CIs due to the smaller sample size.
Supplementary Table S1.
Sensitivity analysis restricted to participants with HbA1c measurements (n=94)
| Variable | aOR (95% CI) | P | ||||
|---|---|---|---|---|---|---|
| Age, years | 0.99 (0.93–1.07) | 0.872 | ||||
| Gender | Female | (ref) | ||||
| Male | 2.52 (0.74–8.61) | 0.140 | ||||
| BMI | <23 | (ref) | ||||
| 23–24.5 | 3.88 (0.69–21.80) | 0.124 | ||||
| ≥25 | 0.44 (0.12–1.57) | 0.203 | ||||
| Duration of diabetes | ≤ 5 years | |||||
| > 5 years | 11.99 (2.90–49.50) | 0.001* | ||||
| Hypertension | Absent | (ref) | ||||
| Present | 1.21 (0.35–4.16) | 0.759 | ||||
| Other Comorbidities | Absent | (ref) | ||||
| Present | 0.18 (0.04–0.82) | 0.027 | ||||
| Blood investigations | Compliant | (ref) | ||||
| Non-compliant | 18.62 (4.42–78.47) | <0.001* | ||||
| Hill–Bone medication adherence scale (0–36) | 0.74 (0.57–0.97) | 0.027 | ||||
BMI=Body mass index, CI=Confidence interval, aOR=Adjusted odds ratio. *P<0.05
The major reasons reported for non-compliance with blood investigations [Figure 2] were inconvenience due to venous sampling, waiting time, forgetfulness, and skipping tests when they feel good.
Figure 2.
Reasons for non-compliance to blood investigations (multiple responses accepted; n = 88)
DISCUSSION
The finding of 59.8% of diabetic patients attending the NCD clinic of an HWC–UPHC having uncontrolled glycemic status aligns with the national patterns, where only 15.7–76.6% achieve control despite treatment.[9,10,12] This high burden underscores persistent implementation gaps in NP-NCD services at the primary care level.
Male gender was found to be a significant risk factor (aOR: 2.59, P = 0.027), corroborating with a study by Mankar et al. reporting 1.63 times higher odds of poor control among males.[13] Possible explanations include biological causes such as increased visceral fat in males leading to higher hepatic insulin resistance compared to females,[25] and behavioral causes due to lower health-seeking behavior and differential medication adherence.[26]
The protective obesity effect (aOR: 0.31, P = 0.008) reflects the well-documented “obesity paradox” in treated type 2 diabetes mellitus, where obese patients receive more aggressive pharmacotherapy and intensive monitoring compared to overweight counterparts.[27,28] This reverse J-shaped BMI–glycemic control relationship has been observed across Asian cohorts.[29]
Diabetes duration >5 years showed a strong association (aOR: 4.55, P < 0.001), matching studies from the UK and India documenting progressive beta-cell failure and treatment resistance beyond this threshold.[8,10]
Non-compliance with blood investigations was the strongest predictor (aOR: 8.47, P < 0.001), with inconvenience, waiting time, and forgetfulness as dominant barriers, corroborating with existing studies.[16,17] These logistical and behavioral factors explain monitoring gaps despite free NP-NCD services. Thus, the health system-related barriers to blood investigations should be addressed further under the NP-NCD.
The sensitivity analysis restricted to participants with available HbA1c measurements further supported the robustness of the primary findings. Diabetes duration greater than 5 years and blood investigation non-compliance remained the strongest determinants of uncontrolled glycemic status, while the direction of association for male gender and obesity was unchanged despite wider CIs attributable to the smaller sample size. These findings suggest that the observed associations were not influenced by the use of different glycemic assessment methods in the primary analysis.
Strengths of the study include the study of a relevant public health topic, the use of standard American Diabetes Association criteria for assessment of glycemic status, validated measures for medication adherence, objective verification of blood investigation records wherever available, comprehensive assessment of barriers to blood investigation compliance, and multivariable logistic regression to identify independent determinants of uncontrolled glycemia. Several limitations should be considered. The cross-sectional design precludes establishing temporal relationships or causal inferences. Selection bias may have occurred because participants were recruited consecutively from two urban primary care NCD clinics, limiting the generalizability of the findings to all people living with type 2 diabetes. Information bias may have arisen from self-reported medication adherence and blood investigation history, although these were verified against available prescriptions and laboratory reports whenever possible. Residual confounding cannot be ruled out, as lifestyle factors such as diet and physical activity, diabetes-related complications, and medication intensification were not included in the final model. Future longitudinal studies incorporating these variables are warranted to better establish causal relationships and evaluate interventions to improve glycemic control. Future interventions should prioritize simplified investigation protocols like point-of-care HbA1c testing to improve blood investigation compliance in order to improve glycemic status among NCD patients. Future studies are required to comprehensively evaluate operational barriers of the NP-NCD at all levels of care.
CONCLUSION
Among NCD clinic attendees in Kalyani, 59.8% had uncontrolled glycemic status, with male gender (aOR: 2.59), diabetes duration >5 years (aOR: 4.55), and blood investigation non-compliance (aOR: 8.47) as key risk factors, while obesity was protective (aOR: 0.31). Addressing logistical barriers through point-of-care testing and targeted interventions can optimize primary care diabetes outcomes under the NP-NCD.
Use of artificial intelligence
The authors used ChatGPT (OpenAI) solely to assist with improving the language, grammar, sentence structure, and overall readability of the manuscript. No AI tool was used to generate or interpret study data, perform statistical analyses, or formulate scientific conclusions. All AI-assisted text was critically reviewed and edited by the authors, who take full responsibility for the final content of the manuscript.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
The authors thank IJCM-IAPSM for providing “IJCM-IAPSM Young researcher award and grant” to one of the authors (JS). The authors thank the project assistants of the Department of Community Medicine and Family Medicine, AIIMS Kalyani - Ms. Neha Parveen and Ms. Rimpa Pal for their assistance in data collection.
Funding Statement
This study is a part of the study funded under “IJCM-IAPSM Young researcher award and grant” awarded to one of the authors (JS).
REFERENCES
- 1.IDF Diabetes Atlas 2025. Diabetes Atlas. Available from: https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/. [Last accessed on 2025 Dec 22]
- 2.Magliano DJ, Boyko EJ, Committee DA 11th Edition Scientific. 3. The global picture of diabetes. Diabetes Atlas. International Diabetes Federation. 11th. 2025. Available from: https://www.ncbi.nlm.nih.gov/books/NBK618744/. [Last accessed on 2025 Dec 22]
- 3.Genitsaridi I, Salpea P, Salim A, Sajjadi SF, Tomic D, James S, et al. 11th edition of the IDF Diabetes Atlas: global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050. Lancet Diabetes Endocrinol. 2026;14:149–56. doi: 10.1016/S2213-8587(25)00299-2. [doi: 10.1016/S2213-8587(25)00299-2] [DOI] [PubMed] [Google Scholar]
- 4.India. International Diabetes Federation. Available from: https://idf.org/our-network/regions-and-members/south-east-asia/members/india/. [Last accessed on 2025 Dec 22]
- 5.Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352:837–53. [PubMed] [Google Scholar]
- 6.Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34) UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352:854–65. [PubMed] [Google Scholar]
- 7.American Diabetes Association Professional Practice Committee for Diabetes*. 6. Glycemic goals, hypoglycemia, and hyperglycemic crises: Standards of care in diabetes—2026. Diabetes Care. 2025;49(Suppl 1):S132–49. doi: 10.2337/dc26-S006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Khunti K, Nikolajsen A, Thorsted BL, Andersen M, Davies MJ, Paul SK. Clinical inertia with regard to intensifying therapy in people with type 2 diabetes treated with basal insulin. Diabetes Obes Metab. 2016;18:401–9. doi: 10.1111/dom.12626. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wang J, Li J, Wen C, Liu Y, Ma H. Predictors of poor glycemic control among type 2 diabetes mellitus patients treated with antidiabetic medications. Medicine (Baltimore) 2021;100:e27677. doi: 10.1097/MD.0000000000027677. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Borgharkar SS, Das SS. Real-world evidence of glycemic control among patients with type 2 diabetes mellitus in India: The TIGHT study. BMJ Open Diabetes Res Care. 2019;7:e000654. doi: 10.1136/bmjdrc-2019-000654. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Duncan BB, Magliano DJ, Boyko EJ. IDF Diabetes Atlas 11th edition 2025: Global prevalence and projections for 2050. Nephrol Dial Transplant. 2026;41:7–9. doi: 10.1093/ndt/gfaf177. [DOI] [PubMed] [Google Scholar]
- 12.Mathur P, Leburu S, Kulothungan V. Prevalence, awareness, treatment and control of diabetes in India from the countrywide national NCD monitoring survey. Front Public Health. 2022;10:748157. doi: 10.3389/fpubh.2022.748157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Mankar A, Kawalkar U, Jadhao N, Joge U, Paldiwal A, Talapalliwar M, et al. Exploring the interplay of socioeconomic and behavioral factors: Unraveling gender disparities in glycemic control among adult type 2 diabetic patients in outpatient care. Cureus. 2024;16:e56505. doi: 10.7759/cureus.56505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.National Programme for Prevention and Control of Non Communicable Diseases (NP-NCD) Guidelines | Ministry of Health and Family Welfare | GOI. Available from: https://www.mohfw.gov.in/?q=en/Major-Programmes/non-communicable-diseases-injury-trauma/Non-Communicable-Disease-II/National-Programme-for-Prevention-and-Control-of-Cancer-Diabetes-Cardiovascular-diseases-and-Stroke-NPCDCS. [Last accessed on 2025 Dec 22]
- 15.National Programme for prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and stroke (NPCDCS) : National Health Mission. Available from: https://nhm.gov.in/index1.php?lang=1andlevel=2andsublinkid=1048andlid=604. [Last accessed on 2025 Dec 22]
- 16.Rahmatullah, Qutubuddin M, Abdul Rahman R, Ghafoor E, Riaz M. Assessment of factors associated with non-compliance to self-management practices in people with type 2 diabetes. Cureus. 2021;13:e18918. doi: 10.7759/cureus.18918. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.S K, K NG, M SR, N SG, P D, Nagaral JV. Treatment adherence and factors contributing to non adherence among type 2 diabetes mellitus patients in a tertiary care hospital: A cross sectional study. Int J Basic Clin Pharmacol. 2017;6:689–94. [Google Scholar]
- 18.HemoCue® HbA1c 501 System. 2024 Jul 09. Available from: https://hemocue.com/in/products/diabetes/hemocue-hba1c-501-system/. [Last accessed on 2025 Dec 24]
- 19.Rathod L, Khan S, Shubham S, Bisne N, Singh S, Kumar M, et al. A comparative evaluation of point-of-care and laboratory HbA1c testing in diabetes care: An Indian perspective. Cureus. 2024;16:e69956. doi: 10.7759/cureus.69956. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ahamed F, Pal D, Palepu S, Somashekara J, Sahoo S, Roy A, et al. Validation of the HemoCue® HbA1c 501 system for point-of-care HbA1c estimation using venous and capillary blood: A comparative study with HPLC in Eastern India. Indian J Community Med. 2026;51:497. doi: 10.4103/ijcm.ijcm_432_25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Misra A. Ethnic-specific criteria for classification of body mass index: A perspective for Asian Indians and American Diabetes Association Position Statement. Diabetes Technol Ther. 2015;17:667–71. doi: 10.1089/dia.2015.0007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kim MT, Hill MN, Bone LR, Levine DM. Development and testing of the Hill-Bone compliance to high blood pressure therapy scale. Prog Cardiovasc Nurs. 2000;15:90–6. doi: 10.1111/j.1751-7117.2000.tb00211.x. [DOI] [PubMed] [Google Scholar]
- 23.M M, M MM, A S, P S, T S, Rodrigues PA. Comparison of medication adherence in patients with type 2 diabetes mellitus: A pre and post study. Int J Basic Clin Pharmacol. 2023;13:107–10. [Google Scholar]
- 24.Kaliyan M, Palaniappan S. Medication adherence of DPP-4 inhibitors in type 2 diabetic management: A Hill-Bone medication adherence scale analysis. Indian J Pharm Educ Res. 2025;59:1231–7. [Google Scholar]
- 25.Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020;63:453–61. doi: 10.1007/s00125-019-05040-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Krämer HU, Rüter G, Schöttker B, Rothenbacher D, Rosemann T, Szecsenyi J, et al. Gender differences in healthcare utilization of patients with diabetes. Am J Manag Care. 2012;18:362–9. [PubMed] [Google Scholar]
- 27.Costanzo P, Cleland JGF, Pellicori P, Clark AL, Hepburn D, Kilpatrick ES, et al. The obesity paradox in type 2 diabetes mellitus: Relationship of body mass index to prognosis. Ann Intern Med. 2015;162:610–8. doi: 10.7326/M14-1551. [DOI] [PubMed] [Google Scholar]
- 28.Singla R, Murthy M, Singla S, Gupta Y. Friendly fat theory – explaining the paradox of diabetes and obesity. Eur Endocrinol. 2019;15:25–8. doi: 10.17925/EE.2019.15.1.25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Prithika UY, Paul CMP, Nethaji VS, Vishnu S, Rumaiza W, Manoharan S, et al. A study on non-compliance to treatment in a Chennai based diabetic population. Int J Community Med Public Health. 2018;5:5465. [Google Scholar]