Diabetes mellitus (DM) represents one of the most difficult non-communicable diseases, remains a considerable risk to health systems worldwide [1]. Diabetes affects numerous people, characterized as chronic hyperglycemia resulting from impairment in insulin secretion and insulin action [2]. The rising prevalence is attributable to swift urbanization, sedentary lifestyle, unhealthy dietary habits, obesity and population aging. Diabetes is a major contributor to morbidity and premature mortality, largely through its microvascular and macrovascular complications [3,4]. Chronic hyperglycemia causes a functional and structural damage in multiple organ systems, including the eye, kidney, nerve system, cardiovascular system and blood vessels [5].

Glycemic control, usually measured in terms of glycated hemoglobin (HbA1c), is crucial for the prevention of these complications [6,7]. HbA1c provides an average blood glucose over the previous 2–3 months and serves as a reliable measure of long-term glycemic control. Sustaining HbA1c levels below recommended targets reduces the risk of diabetic retinopathy, nephropathy, and neuropathy as well as cardiovascular diseases [8]. Poor glycemic control is linked with higher hospitalization rates, lower quality of life, increased health care costs and mortality rate [9]. Effective glycemic control can only be achieved with the appropriate use of pharmacotherapy, lifestyle modification, dietary regulation, regular monitoring and patient education [10].

Diabetes has become a significant public health issue in India. India is often referred to as the “diabetes capital of the world” due to the high and steadily rising number of affected individuals. The burden of Type 2 Diabetes Mellitus (T2DM) is being increasingly in urban areas region, although rural areas can also report rising prevalence [11]. The highest incidence is attributable to environmental factors including genetic predisposition, central obesity and dietary habits with excess carbohydrates intake, physical inactivity as well as socioeconomics transitions [12]. In states like Bihar with low access to healthcare, minimal health literacy and highly heterogeneous socioeconomic status, the use of technologies to improve glycemic control remains a challenge. In addition, other contributing factors such as limited healthcare infrastructure in rural and semi-urban areas, lack of regular follow-up, poor adherence to medication and absence of structured diabetes education programs further compound the problem. However, regional data on glycemic control patterns and corresponding risk factors are still limited.

The current study is conducted at PMCH to assess glycemic control in diabetic patients, as well as determine feasibility correlates of poor glycemic control. A retrospective study design was selected to allows the existing records of patients over a defined duration as it is time and cost-effective and reflects actual clinical practice. This provides important insights into treatment patterns, disease progression and clinical outcomes that does not affect patient management. Moreover, published data from tertiary care centers in Bihar that describe the proportion of patients meeting recommended glycemic targets and factors influencing control are lacking. Identifying predictors such as age, duration of diabetes, type of therapy, body mass index and comorbidities would help in developing specific interventions and guiding more effective diabetes management.

Objectives

• To evaluate glycemic control among diabetic patients attending PMCH.
• To identify the determinants of poor glycemic control.
• To evaluate demographic and clinical predictors

Study Design The current study is a hospital-based retrospective observational study to evaluate glycemic control and determination of attribute for poor Glycemic control among patients with T2DM. A retrospective design was used to analyze existing clinical records without any effect on the patients' management. This method allows to evaluated the clinical practices, treatment outcomes and risk factors in a defined time period. Study Setting The study was conducted in PMCH which was a tertiary care teaching hospital serving to a large population including urban as well as rural areas of Bihar and neighboring states. It offers a comprehensive diabetes service covering outpatient clinic, inpatient management, laboratory investigations and long-term follow-up. Study Duration The study was based on medical records from February 2025 to December 2025. Patient records eligible for review were examined during this period, with data from clinical and laboratory data were extracted for analysis. Sample Size 109 patient records were included in this study. These records fulfilled the predetermined inclusion criteria and contained complete data relevant to the study objectives. As this was a retrospective study, all eligible records that were available during the study period were included. Inclusion Criteria • Diagnosed with T2DM. • Aged 18 years or older. • HbA1c measurement during the study period (February 2025–December 2025). Exclusion Criteria • Patients diagnosed with Type 1 Diabetes Mellitus. • Patients with gestational diabetes. • Diseased records with partial or no critical clinical or laboratory data. Data Collection Data for this retrospective study were retrieved from EMR available in PMCH. A proforma for data extraction of structured data was used to maximize consistency while minimizing bias. Demographic details (age, gender), clinical information (duration of diabetes, BMI), and laboratory parameters (HbA1c, fasting blood sugar and postprandial blood sugar) were gathered from most recent available reports during the study period. Data on treatment modality (oral hypoglycemic agents, insulin or combination therapy), co-morbidities (chronic kidney disease, hypertension and dyslipidemia) and documented medication adherence and lifestyle factors (smoking, alcohol consumption and physical activity) were also extracted, where available. To maintain confidentiality before analysis, all personal identifiers were removed and a unique study code was assigned to each record. Definition of Glycemic Control Glycemic control was assessed by HbA1c levels, indicating the average blood glucose concentration over two to three months. Patients were classified based on the AMA definitions. Good glycemic control was defined as an HbA1c of less than 7%, and poor glycemic control was defined by an HbA1c of equal to or greater than 7%. This classification was used as the primary outcome variable for further statistical analysis and characterization of associated risk factors. Statistical Analysis Data analyzed with SPSS software. Data were summarized using descriptive statistics. Continuous variables are described as mean ± standard deviation (SD), and categorical variables as frequencies and percentages. Data were primarily analysed with Chi-square to evaluate associations between the categorical variables, and independent t-test for comparing difference in means of continuous-variable type data across two groups (good vs poor glycemic control). Multivariate logistic regression analysis was carried out for the identification of independent predictors of poor glycemic control and results were displayed in odds ratios (OR) with their 95% confidence intervals (CI). All analyses were considered statistically significant at p-value less than 0.05.

Baseline Characteristics

A study was conducted on 109 T2DM patients. The average age in the study population was 54.8 ± 11.2 years, and most patients were in the age group of (41–60) years old. The participants consisted of 64 (58.7%) male and 45 (41.3%) female subjects.

Table 1 Demographic Profile of Study Participants

The mean duration of diabetes was 8.6 ± 5.4 years in terms of clinical characteristics. Mean BMI was 26.9 ± 3.8 kg/m². Participants’ mean HbA1c level was 8.2 ± 1.6%. Among co-morbidities, hypertension was the most prevalent (62.4%) and dyslipidemia (48.6%) and chronic kidney disease (14.7%) were the most common.

Table 2 Clinical Characteristics of Study Participants

Glycemic Control Status

According to the HbA1c levels based on the AMA classification criteria, 38 patients (34.9%) had good glycemic control (HbA1c <7%), and 71 patients (65.1%) had poor glycemic control (HbA1c ≥7%). Almost two-thirds of the study population showed suboptimal glycemic control over the course of the study.

Factors Associated with Poor Glycemic Control

Through Analysis, it was demonstrated that there exists a statistically significant relationship between aging and glycemic control, patients above 60 years had the maximum proportion of poor control (p = 0.03). Poor control was slightly more common among males compared with females (19% vs 12%, p = 0.18), but the association was not statistically significant.

In the longer disease duration of diabetes (>10 years), HbA1c was significantly higher than in the shorter disease duration (p = 0.01). In addition, high BMI (≥25 kg/m²) was also significantly associated with poor glycemic control (p = 0.02).

There was significant association with treatment type, where combination therapy and insulin therapy patients had higher proportions of poor control than OHA alone patients (p = 0.04). Hypertension and dyslipidemia were the two most frequent co-morbidities conditions in patient with poor control, but only hypertension difference reached statistical significance (p = 0.03).

Multivariate Logistic Regression Analysis

Multivariate logistic regression analysis was used to determine independent predictors for poor glycemic control. Variables significant in univariate analysis were added to the model.

Table 3 Multivariate Logistic Regression for Predictors of Poor Glycemic Control

In multivariate analysis, longer duration of diabetes, higher BMI, older age and insulin/combination therapy and hypertension were independent significant predictors of poor glycemic control. Duration of diabetes (>10 years) was the significant predictor (Adjusted OR = 2.41, p = 0.01).

Both disease-related and patient-related factors significantly affect glycemic outcomes in this tertiary care population.

This retrospective study demonstrated that more than one third of patients had poor glycemic control. The study population comprised nearly 65.1% of poor glycemic control (HbA1c ≥7%) and only 34.9% who achieved recommended targets. Multivariate analysis revealed longer duration of diabetes, high BMI, old age, insulin or combination therapy and hypertension to be significant independent predictors of poor control. Among these, duration of diabetes more than 10 years was the most potent predictor. These results suggest that both disease progression and modifiable patient-related factors significantly influence the degree of glycemic control.

Comparison with Other Studies

The overall prevalence of poor glycemic control in this study is consistent because with [13] performed on patients attending tertiary care centers that also reported 60–70% of patients not achieving target HbA1c levels. Similar associations with longer duration of diabetes, obesity, and hypertension have also been reported from various other regions in India, indicating a nationwide challenge in achieving optimal diabetes control.

Similarly, [14] show that a significant proportion of patients have uncontrolled diabetes globally, although frequencies differ based on the level of healthcare access and management protocols. Internationally, older age, longer duration of diabetes, obesity and co-morbid conditions are the primary identified predictors of poor glycaemic control. However, some high-income countries report relatively better control rates due to organized follow-up systems and comprehensive diabetes education programs [15]. The current study findings support the global evidence while highlighting a need for better diabetes care strategies in the region.

Clinical Implications

This high rate of poor glycemic control highlights the need for early intervention and frequent monitoring. Early institution of appropriate therapy, tailored treatment regimens and regular HbA1c monitoring are essential to prevent the long-term complications. Lifestyle modification, including dietary regulation, weight management, and increased physical activity plays a vital role in improving glycemic outcomes. In addition, patients education regarding medication compliance as well as self-calibration of blood glucose and warning signs for complications. Enhanced counseling services and organized diabetes education at tertiary care facilities can significantly improve treatment outcomes.

Possible Mechanisms

Several potential mechanisms that could explain the observed associations. With long-term diabetes, there is progressive beta-cell dysfunction and increased insulin resistance that impairs attainment of glycemic goals. The risk of insulin resistance leading to worsening glycemic control and diabetes increased with higher BMI. Another contributory factor may potentially be poor adherence to medication influenced by socio-economic factors, lack of awareness and limited availability to care resources.

Strengths of the Study

This study provides hospital-based data from PMCH, which provides valuable insights on the glycemic control patterns in a tertiary-care setting. It provides local population-based evidence from a geographical area, Bihar, for which published data are available, thus addressing an important gap in knowledge.

Limitations

The retrospective nature limits the capacity to establish causality. The sample size is relatively small (n=109), which may impact generalizability. As a single-center study, these findings may not generalize to all patients. Furthermore, the analysis may have been affected by incomplete documentation of lifestyle factors and medication adherence in some records.

The study found the poor glycemic control was highly prevalence (65.1%) in patients with T2DM. Duration of diabetes, hypertension, high BMI, advanced age and insulin or combination therapy were significant risk factors for mortality. The study's results underline the need for formalized and comprehensive diabetic management programs at PMCH itself to improve metabolic control towards reducing long-term complications. Recommendations HbA1c levels need to be monitored regularly for timely change in treatment. Implementation of a structured diabetes education clinic within the hospital will improve patient awareness and adherence. To target these modifiable risk factors, creating a lifestyle counseling unit for diet, exercise and weight management were recommend. A systematic follow-up registry system would help to ensure further compliance and identify patients at risk of poor glycemic control.

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