Abstract
Abstract
Objective
To assess the burden of diabetes and prediabetes in the educational sector in Bahawalpur City, Pakistan.
Design
Cross-sectional study.
Setting
Teaching institutes of Bahawalpur, Pakistan, during January 2024 to December 2024.
Methods
A total of 955 participants from 15 universities, colleges and schools were included. Eligible participants were aged 18–75 years and employed as teachers or academic staff and enrolled using a non-probability consecutive sampling technique. Primary anthropometric measurements, blood pressure, smoking status and HbA1c levels were recorded. Prediabetes was defined as HbA1c 5.7–6.4% and type 2 diabetes mellitus (T2DM) as HbA1c ≥6.5%.
Results
Among 955 participants, 622 (65.1%) were male and 713 (74.7%) were teaching staff. The median age was 42 years, and median BMI was 27.3 kg/m². The prevalence of prediabetes and T2DM was 31.7% and 15.4%, respectively, with 8.5% newly diagnosed cases of T2DM. Multivariate binary logistic regression analysis found that age (p=0.006), BMI (p=0.008) and family history of diabetes (p<0.001) were independent predictors of prediabetes/T2DM. Males had a higher median HbA1c (5.60%, IQR: 5.20–6.30) than females (5.40%, IQR: 5.20–5.80). HbA1c levels rose with age: 6.10% (IQR: 5.75–8.95) in ages 61–75, 5.80% (IQR: 5.40–6.80) in 46–60 and 5.35% (IQR: 5.00–5.70) in 18–45. Higher education was associated with lower HbA1c (above master’s: 5.40%, IQR: 5.10–5.80). No significant HbA1c differences were found by occupation (p=0.711) or income (p=0.655). Hypertensive individuals had a higher HbA1c (5.90%, IQR: 5.40–6.85) versus non-hypertensives (5.50%, IQR: 5.10–5.90), and those with a family history also had elevated HbA1c (5.70%, IQR: 5.30–6.50) versus those without (5.40%, IQR: 5.10–5.90). HbA1c correlated moderately with age (r=0.388) and weakly with BMI (r=0.228) and waist circumference (r=0.254) (all p<0.001).
Conclusions
This study highlights a significant prevalence of T2DM and prediabetes in the educational sector of Bahawalpur, Pakistan. Increasing age, BMI and positive family history of diabetes were independent predictors of prediabetes/T2DM.
Keywords: Hypertension; Body Mass Index; Diabetes Mellitus, Type 2
STRENGTHS AND LIMITATIONS OF THIS STUDY.
A large, well-defined sample (n=955) across 15 educational institutions enhances generalisability within the academic workforce.
Use of HbA1c reduces measurement bias and inclusion of both anthropometric and clinical variables for risk profiling.
Identification of newly diagnosed diabetes cases strengthens public health relevance.
Cross-sectional design limits causal inference, and single-city focus restricts generalisability to other regions or non-academic populations.
Lack of dietary, physical activity and stress-related data limits assessment of modifiable lifestyle factors.
Introduction
According to the International Diabetes Federation Diabetes Atlas 2024, an estimated 34.4% adults in Pakistan suffer from type 2 diabetes mellitus (T2DM), with a significant proportion remaining undiagnosed.1 In addition to diabetes, prediabetes remains largely underdiagnosed, despite its strong association with a heightened risk of progressing to overt diabetes and cardiovascular complications. Hyperglycaemia has become a growing global health concern as its prevalence has surged over the past few decades, largely driven by lifestyle modifications, urbanisation and changes in dietary habits.2 South Asian populations, including Pakistan, have been identified as particularly vulnerable to diabetes due to genetic predisposition and environmental factors, further exacerbated by the region’s rapid epidemiological transition.3 4
While much research has focused on hyperglycaemia in general populations, there is limited data on its prevalence among specific professional groups, particularly educated professionals such as teachers and academic staff. Educated professionals are often assumed to have better health awareness and access to medical care.5 Teachers, in particular, spend long hours engaged in desk work, lesson planning and administrative tasks, which may inadvertently promote unhealthy lifestyles.6 The prevalence of unhealthy dietary habits, irregular meal timings and high consumption of processed or fast food due to work-related time constraints may further contribute to the risk of hyperglycaemia in this population.7 8 Chronic metabolic disorders can lead to fatigue, reduced productivity, cognitive impairment and increased absenteeism, ultimately affecting job performance.9 10 In the long run, untreated hyperglycaemia can lead to severe complications such as neuropathy, nephropathy, retinopathy and cardiovascular disease, significantly impairing the quality of life.11 12 There is a pressing need to assess the prevalence of hyperglycaemia among educated professionals, particularly those working in teaching institutes, where occupational stress and sedentary lifestyles are prevalent.13
Bahawalpur, located in the South Punjab region of Pakistan, is a prominent educational hub, housing numerous universities, colleges and schools. While national surveys and community-based studies in Pakistan have extensively documented the rising prevalence of diabetes and metabolic disorders in general populations, such as 10.0% for T2DM and 11.0% for prediabetes in pooled analyses,14 there is limited evidence focusing on occupational or profession-based cohorts. The Diabetes Prevalence Survey of Pakistan reported mean HbA1c levels of 5.62% and identified the highest prevalence in the 51–60 years age group.15 Parallel to this, metabolic syndrome regional data from institutional staff have demonstrated elevated prevalence among academic staff compared with student populations, emphasising that workplace, lifestyle and sedentary behaviour can influence cardiometabolic risk in educated professionals.16 In Pakistan, few studies have targeted teachers or academic professionals. One quasi-experimental study in Sindh evaluated the effectiveness of diabetes health education among public school teachers, illustrating both the feasibility and the need for more rigorous data in this demographic.17 Because teachers tend to have distinctive sedentary schedules, stress profiles and health awareness compared with the general population, they may represent a higher-risk group or a neglected one in screening efforts. Comparing hyperglycaemia burden within a professionally homogeneous group can reduce confounding by socioeconomic heterogeneity. Therefore, this study aims to bridge the evidence gap and may inform targeted screening strategies and preventive interventions tailored to the academic workforce. The current study intentionally focused on quantifiable clinical and anthropometric variables rather than behavioural or lifestyle factors in order to ensure objective measurement and minimise recall bias. By emphasising measurable parameters, this study provides a reliable estimate of the hyperglycaemia burden and its biomedical correlates within the educational workforce. The main objective of this study was to determine the burden of diabetes and prediabetes in the Educational Sector in Bahawalpur City of Pakistan.
Material and methods
Study design
This was a cross-sectional, observational study designed to evaluate the burden of diabetes and prediabetes and their associated risk factors among academic professionals in Bahawalpur, Pakistan.
Setting
A total of 15 educational institutions were included in this study through institutional permission and voluntary participation of staff. These comprised a mix of five universities, six colleges and four schools (secondary or higher-secondary level) located within Bahawalpur City, South Punjab, Pakistan. The selection was made to ensure representation across different tiers of the educational system, reflecting diverse occupational and socioeconomic backgrounds within the academic workforce. All institutions were registered and recognised by the respective provincial education authorities or higher education bodies. Data collection occurred over a 1-year period, from January 2024 to December 2024.
Participants
Participants included academic and professional staff of any gender, aged 18 to 75 years, currently employed at the selected teaching institutions. Individuals were recruited through voluntary participation and informed consent. A non-probability consecutive sampling technique was used enrolling all eligible and available participants. Participants were divided into two occupational categories as teaching staff and non-teaching staff. Teaching staff included individuals directly involved in educational or academic activities such as lecturers, professors and schoolteachers. Non-teaching staff comprised employees performing administrative, clerical, technical or support roles within the same institutions but not engaged in instructional duties.
Sample size
Considering the anticipated proportion of T2DM in the local population as 34.4%,1 with a 95% confidence level and 4% margin of error, the minimum required sample size was calculated to be 542. To enhance precision, the final sample size was 955 participants.
Inclusion criteria
Any gender.
Aged between 18 and 75 years.
Employed as teaching or non-teaching (including administrative, technical or support personnel) staff.
Willing to provide written informed consent and undergo laboratory investigations.
Exclusion criteria
Pregnant women.
Individuals with a history of major surgery or severe illness in the past 3 months.
Individuals unwilling to participate or undergo laboratory testing.
Data collection procedures
A structured proforma was used to collect relevant data from all participants. Demographic details (age, gender, education level, monthly income), occupational status (teaching vs non-teaching) and smoking history were documented. All participants underwent a standardised health examination conducted within the premises of their respective educational institutions. The examination team comprised two trained medical officers and one qualified laboratory technologist. Each participant was first evaluated for basic anthropometric and clinical parameters, including height, weight, BMI and waist circumference, using calibrated measuring equipment according to WHO protocols. Blood pressure was measured in a seated position after at least 5 min of rest using a validated digital sphygmomanometer, two readings were taken 5 min apart and the average value was recorded. Venous blood samples (3–5 mL) were collected under aseptic conditions by a trained phlebotomist after an overnight fast of 8–10 hours. Samples were placed in EDTA tubes, labelled with unique participant codes, and immediately transported in temperature-controlled containers (2–8°C) to the collaborating institutional clinical laboratory for analysis on the same day. HbA1c was analysed using the high-performance liquid chromatography method. Internal and external quality controls were performed to ensure analytical accuracy and precision. Individuals with known diabetes had their disease duration documented.
Variables and definitions
Prediabetes: defined as HbA1c between 5.7% and 6.4%.18
Diabetes mellitus (T2DM): defined as HbA1c ≥6.5%0.19
Normoglycaemia: defined as HbA1c <5.7%0.18
Hypertension: self-reported history or documented elevated blood pressure (systolic ≥140 mm Hg or diastolic ≥90 mm Hg).20
Waist circumference: Waist circumference was measured using a non-stretchable measuring tape at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, with the participant standing erect, abdomen relaxed and at the end of gentle expiration. Measurements were taken twice to the nearest 0.1 cm, and the mean value was recorded for analysis.
Statistical analysis
Data were analysed using IBM SPSS Statistics V. 26.0. Continuous variables were summarised as mean±SD for normally distributed data or median with IQR for skewed data, while categorical variables were presented as frequencies and percentages. Group comparisons were made using the χ2 test or Fisher’s exact test for categorical variables and the independent sample t-test, Mann-Whitney U test or Kruskal-Wallis test for continuous variables, as appropriate. Pearson or Spearman correlation was applied to evaluate associations between continuous variables. Initially, crude (univariate) analyses were performed to identify variables associated with hyperglycaemia. Variables with p<0.20 in the univariate analysis were subsequently included in a multivariate binary logistic regression model to determine adjusted associations and identify independent predictors of hyperglycaemia (prediabetes/diabetes). Adjusted odds ratios (AOR) with 95% CIs were reported, and a p value <0.05 was considered statistically significant.
Results
Characteristics of participants
A total of 955 participants were analysed. Out of these 955, 622 (65.1%) were males and 333 (34.9%) females. The median age was 42.00 years (35.00–52.00 years), ranging between 18 and 74 years. The monthly income of 676 (70.8%) participants was above 70 000 PKR. There were 713 (74.7%) participants who were teaching staff, while the remaining 242 (25.3%) were non-teaching staff. The median BMI was 27.30 kg/m2 (24.50–30.50 kg/m2). The median HbA1c was 5.50% (5.10–6.00%). Hypertension was identified in 109 (11.4%) participants. Family history of diabetes was reported by 255 (26.7%) participants. There were 303 (31.7%) patients who were classified as prediabetic and 147 (15.4%) as having diabetes mellitus. There were 81 (8.5%) patients who were newly T2DM.
Association of hyperglycaemia with characteristics of participants
Gender distribution showed a significant association with glycaemic status, as the proportion of males increased from normoglycaemia (60.8%), prediabetes (65.7%) to diabetes mellitus (78.9%) (p<0.001). Increasing age was found to have significant association with prediabetes and diabetes mellitus (p<0.001). BMI (p<0.001) and waist circumference (p<0.001) were significantly high among participants having diabetes mellitus and prediabetes when compared with normoglycaemic individuals. Education level showed a significant association with glycaemic status (p<0.001), with the prevalence of diabetes being higher among participants with relatively lower education levels. There was no statistically significant difference in glycaemic status based on occupation (p=0.280). Monthly income did not show a significant association with glycaemic categories (p=0.843). Smoking history was not significantly associated with glycaemic status (p=0.898). Hypertension showed a strong association, increasing from 6.7% in normoglycaemic individuals to 13.9% in prediabetics and 22.4% in diabetics (p<0.001). A positive family history of diabetes was significantly associated with worsening glycaemic status, with 21.4% of normoglycaemic individuals having a family history compared with 28.1% in prediabetics and 42.2% in diabetics (p<0.001). Associations of glycaemic categories with characteristics of participants are shown in table 1.
Table 1. Association of hyperglycaemia categories with characteristics of participants (n=955).
| Characteristics | Normoglycaemia (n=505) | Prediabetes (n=303) | Diabetes mellitus (n=147) | P value | |
|---|---|---|---|---|---|
| Gender | Male | 307 (60.8%) | 199 (65.7%) | 116 (78.9%) | <0.001 |
| Female | 198 (39.2%) | 104 (34.3%) | 31 (21.1%) | ||
| Age (years) | 18–45 | 369 (73.1%) | 158 (52.1%) | 45 (30.6%) | <0.001 |
| 46–60 | 135 (26.7%) | 142 (46.9%) | 98 (66.7%) | ||
| 61–75 | 1 (0.2%) | 3 (1.0%) | 4 (2.7%) | ||
| Age (years) | 40.00 (33.00–46.00) | 44.50 (38.00–52.00) | 51.00 (43.00–55.25) | <0.001 | |
| Body mass index (kg/m2) | 26.70 (23.60–29.30) | 28.03 (25.45–31.47) | 28.25 (25.00–31.51) | <0.001 | |
| Waist circumference (cm) | 36.50 (34.00–39.00) | 38.00 (37.00–40.00) | 39.00 (36.00–40.00) | <0.001 | |
| Education | Up to Graduation | 42 (8.3%) | 21 (6.9%) | 15 (10.2%) | <0.001 |
| Masters | 204 (40.4%) | 160 (52.8%) | 84 (57.1%) | ||
| Above masters | 259 (51.3%) | 122 (40.3%) | 48 (32.7%) | ||
| Occupation | Teaching staff | 368 (72.9%) | 236 (77.9%) | 109 (74.1%) | 0.280 |
| Non-teaching staff | 137 (27.1%) | 67 (22.1%) | 38 (25.9%) | ||
| Monthly income (PKR) | ≤70 000 | 147 (29.1%) | 86 (28.4%) | 46 (31.3%) | 0.814 |
| >70 000 | 358 (70.9%) | 217 (71.6%) | 101 (68.7%) | ||
| History of smoking | Current smoker | 12 (2.4%) | 7 (2.3%) | 4 (2.7%) | 0.898 |
| Former smoker | 8 (1.6%) | 3 (1.0%) | 1 (0.7%) | ||
| Never smoker | 485 (96.0%) | 293 (96.7%) | 142 (96.6%) | ||
| Hypertension | 34 (6.7%) | 42 (13.9%) | 33 (22.4%) | <0.001 | |
| Family history of diabetes | 108 (21.4%) | 85 (28.1%) | 62 (42.2%) | <0.001 | |
A binary logistic regression was performed to identify independent predictors of prediabetes or T2DM. The model included gender, age, BMI, waist circumference, education level, hypertension and family history of diabetes. The model was statistically significant (χ²=48.58, df=8, p<0.001), indicating that the included variables reliably distinguished between normoglycaemic and prediabetes/T2DM individuals. The Hosmer-Lemeshow goodness-of-fit test (χ²=10.57, p=0.227) confirmed that the model fit the data adequately. Among the included predictors, age (p=0.006), BMI (p=0.008) and family history of diabetes (p<0.001) were statistically significant independent predictors of prediabetes/T2DM. The adjusted odds of prediabetes/T2DM increased by 4% with each additional year of age (AOR 1.04, 95% CI 1.01 to 1.07) and by 12% for each unit increase in BMI (AOR 1.12, 95% CI 1.03 to 1.22). Participants with a positive family history of diabetes had 3.9-fold higher odds of being prediabetes/T2DM (AOR 3.91, 95% CI 1.86 to 8.22). Gender, waist circumference, education and hypertension did not reach statistical significance after adjusting for confounders (table 2).
Table 2. Multivariate binary logistic regression for predictors of prediabetes/T2DM.
| Variables | P value | Adjusted OR | 95% CI for adjusted OR |
|---|---|---|---|
| Gender (male) | 0.098 | 1.69 | 0.91–3.13 |
| Age in years | 0.006 | 1.04 | 1.01–1.07 |
| Body mass index in kg/m2 | 0.008 | 1.12 | 1.03–1.22 |
| Waist circumference in cm | 0.710 | 1.01 | 0.93–1.11 |
| Education up to intermediate | 0.788 | 1.13 | 0.46–2.78 |
| Education up to masters | 0.658 | 1.14 | 0.64–2.04 |
| Hypertension | 0.422 | 1.42 | 0.60–3.35 |
| Family history of diabetes | <0.001 | 3.91 | 1.86–8.22 |
HbA1c distribution with respect to characteristics of participants
Males had a significantly higher median HbA1c (5.60%, IQR: 5.20–6.30) compared with females (5.40%, IQR: 5.20–5.80) (p<0.001). Age was strongly associated with HbA1c levels, with the highest median observed in individuals aged 61–75 years (6.10%, IQR: 5.75–8.95), followed by those aged 46–60 years (5.80%, IQR: 5.40–6.80), while the youngest group (18–45 years) had the lowest median (5.35%, IQR: 5.00–5.70) (p<0.001). Education level showed a significant association with HbA1c (p<0.001), where individuals with the highest education level (above master’s) had lower median HbA1c (5.40%, IQR: 5.10–5.80) compared with those with a master’s degree (5.60%, IQR: 5.20–6.20) and those with education up to graduation (5.50%, IQR: 5.20–6.18). No significant differences were observed in HbA1c levels between teaching and non-teaching staff (p=0.711) or monthly income (p=0.655). Individuals with hypertension had a median HbA1c of 5.90% (IQR: 5.40–6.85) compared with 5.50% (IQR: 5.10–5.90) in those without hypertension (p<0.001). Those with a family history of diabetes had a higher median HbA1c (5.70%, IQR: 5.30–6.50) compared with those without a family history (5.40%, IQR: 5.10–5.90) (p<0.001). Table 3 shows the details about the association of HbA1c distribution with various study variables.
Table 3. HbA1c distribution with respect to characteristics of participants (n=955).
| Characteristics | HbA1c (%) median (IQR) | P value | |
|---|---|---|---|
| Gender | Male | 5.60 (5.20–6.30) | <0.001* |
| Female | 5.40 (5.20–5.80) | ||
| Age (years) | 18–45 | 5.35 (5.00–5.70) | <0.001† |
| 46–60 | 5.80 (5.40–6.80) | ||
| 61–75 | 6.10 (5.75–8.95) | ||
| Education | Up to Graduation | 5.50 (5.20–6.18) | <0.001† |
| Masters | 5.60 (5.20–6.20) | ||
| Above masters | 5.40 (5.10–5.80) | ||
| Occupation | Teaching staff | 5.50 (5.10–6.10) | 0.711* |
| Non-teaching staff | 5.50 (5.10–6.10) | ||
| Monthly income (PKR) | ≤70 000 | 5.50 (5.20–6.05) | 0.655† |
| >70 000 | 5.50 (5.00–6.20) | ||
| History of smoking | Current smoker | 5.30 (5.10–6.00) | 0.600† |
| Former smoker | 5.35 (5.00–6.00) | ||
| Never smoker | 5.50 (5.10–6.00) | ||
| Hypertension | Yes | 5.90 (5.40–6.85) | <0.001* |
| No | 5.50 (5.10–5.90 | ||
| Family history of diabetes | Yes | 5.70 (5.30–6.50) | <0.001* |
| No | 5.40 (5.10–5.90) | ||
| Glycaemic categories | Normoglycaemia | 5.20 (4.90–5.40) | <0.001† |
| Prediabetes | 5.90 (5.70–6.20) | ||
| Diabetes mellitus | 8.30 (6.90–10.00) | ||
Mann-Whitney U test applied
Kruskal-Wallis test applied
Relationship of HbA1c with age, BMI and waist circumference
Age showed a moderate positive correlation with HbA1c (r=0.388, p<0.001), as shown in figure 1. BMI demonstrated a significant but relatively weaker correlation with HbA1c (r=0.228, p<0.001), as shown in figure 2. Waist circumference was positively correlated with HbA1c (r=0.254, p<0.001), as shown in figure 3.
Figure 1. Scatter plot showing correlation of HbA1c with age.
Figure 2. Scatter-plot showing correlation of HbA1c with body mass index.
Figure 3. Scatter plot showing correlation of HbA1c with waist circumference.
Discussion
The prevalence of prediabetes (31.7%) and diabetes mellitus (15.4%) was substantial, with 8.5% newly diagnosed cases of T2DM, highlighting a considerable proportion of previously unrecognised dysglycaemia within this occupational group. Increasing age, higher BMI and a positive family history of diabetes emerged as independent predictors of hyperglycaemia. In crude analyses, several additional factors including male gender, higher waist circumference, lower education level and hypertension were significantly associated with hyperglycaemia status; however, these associations did not remain statistically significant after adjustment for potential confounders.
The observed prevalence of diabetes mellitus (15.4%) was higher than estimates reported in the CLUSTer cohort study from Malaysia, which identified a prevalence of 4.1% for diagnosed diabetes and 5.1% for undiagnosed diabetes among school teachers.21 The proportion of prediabetes in the Malaysian cohort was significantly lower (5.6%) than the prediabetes prevalence observed in the present study (31.7%). Ketata et al22 conducted a study among secondary school teachers in Tunisia, and the prevalence of diabetes was 8.9%, while prediabetes was reported in 10.5% of participants. Lone et al,23 in Nagpur, India, reported a diabetes prevalence of 14.6%, closely resembling the findings of this study. Both Ketata et al and Lone et al studies emphasised that chronic occupational stress, sedentary lifestyles and high BMI contributed to metabolic disorders among academic professionals.22 23 The higher prevalence of hyperglycaemia in the current study may reflect regional differences in metabolic risk profiles, variations in genetic predisposition and lifestyle disparities. South Asian populations, including Pakistanis, are known to have a higher genetic susceptibility to insulin resistance and metabolic disorders compared with Southeast Asian populations.24 25 Dietary habits, levels of physical activity and access to healthcare may contribute to these disparities.
Age demonstrated a strong correlation with hyperglycaemia and the median HbA1c progressively increased with age, reaching 6.10% (IQR: 5.75–8.95) in individuals aged 61–75 years. These findings are consistent with global trends demonstrating that advancing age is a key determinant of impaired glucose metabolism due to progressive pancreatic beta-cell dysfunction and increased insulin resistance.26 27
The significant correlation between BMI and HbA1c levels (r=0.228, p<0.001) suggests a potential role of targeting obesity prevention as a primary strategy for diabetes control. Obesity, particularly central adiposity, is a major driver of insulin resistance and metabolic dysfunction, as was highlighted in the CLUSTer study.21 Interventions focusing on weight management, dietary modifications and increased physical activity may be prioritised in diabetes prevention strategies.28 29
The prevalence of diabetes was highest among individuals with education up to graduation or above. These findings are different when compared with Ketata et al22 who did not observe a significant relationship between educational status and diabetes prevalence. One possible explanation for the current study’s results is that higher education levels may be associated with greater health awareness, access to preventive healthcare and better dietary and lifestyle practices.28 Individuals with advanced degrees may have occupations that afford greater flexibility for incorporating healthier behaviours, including regular medical check-ups and structured physical activity routines.
Hypertension is a well-established comorbidity of diabetes, with both conditions sharing common pathophysiological mechanisms, including endothelial dysfunction, chronic inflammation and renin-angiotensin system dysregulation.30,32 Azuogu et al from Nigeria similarly found that hypertension was significantly associated with diabetes prevalence among school teachers.33 These findings underscore the need for integrated screening programmes for both conditions, particularly in high-risk occupational groups.
A family history of diabetes was also significantly associated with prediabetes/T2DM (AOR 3.91, 95% CI 1.86 to 8.22). This association aligns with existing literature, emphasising that genetic predisposition remains a key determinant of metabolic health.34 35 Regional data from India also reported a high prevalence of diabetes among individuals with a family history of the disease.23 Given this hereditary link, targeted interventions focusing on early screening and lifestyle modifications are particularly critical for individuals with a strong genetic predisposition.36
The high prevalence of prediabetes and diabetes observed among employees of educational institutions highlights the need for workplace-based screening and early detection strategies in the educational sector. Given that the majority of participants were teachers, educational institutions represent an important setting for institutional health initiatives, including periodic glucose screening, risk assessment for metabolic disorders and awareness programmes on diabetes prevention. Such workplace-based approaches could facilitate early identification of individuals at risk and support timely referral for clinical management, thereby helping to reduce the long-term burden of diabetes in this professional group.
This study is among the first in Pakistan to specifically evaluate the burden of prediabetes and diabetes within the educational sector, encompassing both teaching and non-teaching staff across multiple tiers of education. By targeting a professionally homogeneous yet under-studied group, it addresses a critical knowledge gap overlooked by prior national and regional surveys that primarily focused on community or hospital-based populations. Regarding the strengths of this study, a large, well-defined sample (n=955) across 15 educational institutions enhances generalisability within the academic workforce. Use of HbA1c, a standardised and reliable biomarker, reduces measurement bias. The inclusion of both anthropometric and clinical variables allows comprehensive risk profiling.
This study has several limitations. The cross-sectional design precludes the establishment of causality between risk factors and hyperglycaemia. Data on dietary habits, physical activity levels and stress indices were not collected, which may have provided additional insights into behavioural risk factors contributing to hyperglycaemia. The study population was limited to teaching professionals in Bahawalpur, which may limit the generalisability of findings to other occupational groups or regions. The study relied on HbA1c as the sole marker of hyperglycaemia, without incorporating fasting blood glucose or oral glucose tolerance tests, which could have provided a more comprehensive assessment of hyperglycaemia.
Conclusion
This study highlights a significant prevalence of T2DM and prediabetes in the educational sector of Bahawalpur, Pakistan. Increasing age, BMI and positive family history of diabetes were independent predictors of prediabetes/T2DM. Given the strong association between BMI and hyperglycaemia, obesity prevention may be prioritised as a key strategy in diabetes risk reduction. These results highlight the need for targeted workplace interventions, routine metabolic screenings and structured lifestyle modification programmes within academic institutions. Future research should focus on longitudinal studies to better understand the progression of hyperglycaemia and evaluate the impact of preventive interventions in high-risk occupational groups.
Acknowledgements
The authors thank the study participants who participated in this study as well as the administrative staff of the teaching institutes involved in this study. The authors also thank Muhammad Aamir Latif (RESnTEC) for his assistance in statistical analysis of this research.
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Prepublication history for this paper is available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2025-115283).
Patient consent for publication: Not applicable.
Ethics approval: The study was approved by the Hospital Research Committee of Bahawalpur (Approval Letter No. HRC/14/2023, dated: 12-08-2023). All procedures adhered to institutional and international ethical guidelines for human research. There was no direct patient or public involvement in the design, conduct, reporting, or dissemination of this research. Participants were informed about the study objectives and gave voluntary written informed consent prior to enrolment.
Provenance and peer review: Not commissioned; externally peer reviewed.
Collaborators: None.
Patient and public involvement: Participants were informed about the study objectives and gave voluntary written informed consent prior to enrolment.
Data availability statement
Data are available upon reasonable request.
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