Abstract
BACKGROUND:
Medical students, as future healthcare professionals, should model healthy lifestyle behaviors. However, their own practices and health profiles often fall short of optimal standards. This study assessed anthropometric measures, lifestyle habits, and clinical risk factors for non-communicable diseases among Kurdish medical students.
MATERIALS AND METHODS:
A cross-sectional study was conducted among 280 undergraduate medical students (80 males, 200 females) aged 18–24 years at Hawler Medical University. Data were collected using a two-part approach: (1) direct clinical assessments for anthropometrics (height, weight), blood pressure (BP), and random blood glucose (RBG); and (2) a pre-validated, structured questionnaire to document diet, physical activity, smoking, and sleep patterns. Body Mass Index (BMI) was calculated and categorized. Gender-based differences and correlations were analyzed.
RESULTS:
A normal BMI was observed in 59.3% of students; however, 22.1% were overweight, 8.6% obese, and 10% underweight. Hypertension prevalence was high (56.4%), while most participants had normal RBG (92.9%). Male students had significantly higher BMI, SBP, DBP, and less healthy dietary habits compared to females (P < 0.001). Positive correlations were found between BMI and both Systolic Blood Pressure (SBP) (r = 0.30) and Diastolic Blood Pressure (DBP) (r = 0.22), but not with Random Blood Glucose (RBG). High BMI was also associated with hypertension and poor sleep.
CONCLUSION:
Despite most students having a normal BMI, a substantial proportion exhibited elevated blood pressure, overweight, and obesity, underscoring the need for targeted lifestyle interventions and health promotion within medical education.
Keywords: Body mass index, hypertension, lifestyle, medical student, noncommunicable diseases, risk factors
Introduction
The global burden of non-communicable diseases (NCDs), particularly those linked to metabolic risk factors and lifestyle, is increasing rapidly.[1] According to the World Health Organization (WHO), obesity has nearly tripled worldwide, with elevated body mass index (BMI) recognized as a major modifiable risk factor contributing to morbidity and mortality. Among the many health complications associated with obesity, elevated blood pressure (BP) is particularly concerning due to its direct link with stroke, heart failure, and renal disease.[2]
Numerous studies across different populations have consistently reported a positive correlation between excess body weight and elevated BP. In Iraq, especially in the Kurdistan Region, rapid socio-economic changes have altered lifestyle patterns, potentially increasing susceptibility to obesity-related health risks.[3]
The demanding nature of medical education may further place students at risk of unhealthy behaviors such as stress-induced eating, sedentary lifestyles, and poor sleep, all of which contribute to obesity and hypertension.[4] Understanding health risk factors in this population is critical, not only for their personal well-being but also because, as future healthcare providers, their attitudes and behaviors can influence wider public health trends.[5]
This study aimed to assess the prevalence of overweight, obesity, and hypertension among undergraduate medical students in the Kurdistan Region. It also explored the relationship between BMI and blood pressure, along with associated lifestyle factors. The findings are intended to inform institutional strategies that promote healthier habits among future physicians [Figure 1].
Figure 1.
Conceptual framework
While BMI and blood pressure have been studied in other medical student populations, data specific to Kurdish students remain limited. We hypothesized that Kurdish medical students would show high rates of overweight, obesity, and hypertension, linked to adverse lifestyle factors and academic stress.
Study novelty
This is among the first studies to comprehensively evaluate anthropometric, lifestyle, and clinical factors among Kurdish medical students. Unlike earlier work that primarily reported prevalence, our study highlights gender-based differences and reveals an unexpectedly high prevalence of hypertension in a young, educated population. These results provide context-specific evidence to guide preventive strategies and institutional health promotion programs within medical education settings.
Materials and Methods
Study design and setting
This cross-sectional study was conducted at Hawler Medical University, Erbil, Iraq, between February and October 2024. Study ID: HMU-CME-2024-01.
Study participants and sampling
A total of 280 undergraduate medical students aged 18–24 years participated in the study. Of these, 28.6% were male and 71.4% female. Participants were recruited from different academic years using a convenience sampling approach.
Data collection tool and technique
Anthropometric measurements (height, weight, BMI), blood pressure (systolic and diastolic), and random blood glucose (RBG) were obtained through direct clinical assessments. Additionally, a structured questionnaire was administered to collect data on lifestyle behaviors, including diet, physical activity, smoking, and sleep patterns.
Ethical consideration
Ethical approval was obtained from the Ethics Committee of Hawler Medical University, College of Health Sciences (Ethical code number: Sc.E.C. 6B). Informed consent was obtained from all participants, and confidentiality of information was maintained throughout the study.
Statistical analysis
Data were analyzed using SPSS version 26.0. Results were expressed as means ± standard deviations (SD) or medians with interquartile ranges (IQR). Chi-square and Mann–Whitney U tests were applied for categorical and continuous variables, respectively. Correlations were examined using Spearman’s rank test. The normality of continuous data was formally assessed using the Shapiro–Wilk test. As the data for key variables (BMI, BP) deviated from a normal distribution (P < 0.05), non-parametric tests (Mann–Whitney U, Kruskal–Wallis) were applied. Prevalence rates are reported with their 95% confidence intervals (95% CI).
Results
Among the 280 participants (mean age 21.4 ± 2.2 years), 59.3% had normal BMI, 22.1% were overweight, 8.6% obese, and 10% underweight, indicating both undernutrition and excess weight. Hypertension was present in 56.4% of students, with only 39.3% normotensive. Most (92.9%) had normal blood glucose levels.
Lifestyle habits were suboptimal: 11.4% smoked, 36.4% regularly consumed fast food, and 57.9% did not exercise. While most consumed adequate carbohydrates (98.6%) and protein (97.9%), only 27.1% reported high water intake. Sleep duration was generally adequate in 79.3% of students [Table 1]. Table 2 shows male students had significantly higher BMI, SBP, DBP, and reported more frequent fast-food consumption and greater water intake. Females generally had longer sleep durations. Obesity was more common among males (15%), while underweight status was more common among females (12%).
Table 1.
Clinical, socio-demographic, and anthropometric data
| Characteristics | Mean±SD | No. of subjects, (n=280) | Percentage (%) | |||
|---|---|---|---|---|---|---|
| Age | 21.4±2.2 | 280 | (100) | |||
| Gender | ||||||
| Male | 80 | (28.6) | ||||
| Female | 200 | (71.4) | ||||
| Height (cm) | 165.74±8.06 | |||||
| Weight (kg) | 64.69±14.39 | |||||
| BMI (kg/m2) | 23.45±4.34 | |||||
| Normal | (59.3) | |||||
| Overweight | (22.1) | |||||
| Obese | (8.6) | |||||
| Underweight | (10) | |||||
| RBG (mg/dL) | ||||||
| Hypoglycemia | 10 | (3.6) | ||||
| Normoglycemia | 104.03±21.62 | 260 | (92.9) | |||
| Hyperglycemia | 10 | (3.6) | ||||
| SBP (mmHg) | 114.41±14.29 | |||||
| DBP (mmHg) | 75.16±12.19 | |||||
| BP status (mmHg) | 115.17±14.34 | |||||
| Normotensive | (39.3) | |||||
| Hypertensive | (56.4) | |||||
| Smoking behavior | ||||||
| Smoker | 32 | (11.4) | ||||
| Non-smoker | 248 | (88.6) | ||||
| Smoke type | ||||||
| Cigarette | 20 | (7.1) | ||||
| Others | 12 | (4.3) | ||||
| Meal frequency | ||||||
| Rare | 92 | (32.9) | ||||
| Standard | 160 | (57.1) | ||||
| Frequent | 28 | (10.0) | ||||
| Daily-carb-consumption | ||||||
| Yes | 276 | (98.6) | ||||
| No | 4 | (1.4) | ||||
| Daily-protein-consumption | ||||||
| Yes | 274 | (97.9) | ||||
| No | 6 | (2.1) | ||||
| Oil-type-consumption | ||||||
| Saturated | 26 | (9.3) | ||||
| Unsaturated | 216 | (77.1) | ||||
| Both | 38 | (13.6) | ||||
| Fast food-consumption | ||||||
| Occasional | 72 | (25.7) | ||||
| Regular | 102 | (36.4) | ||||
| Frequent | 58 | (20.7) | ||||
| Water Intake | ||||||
| Low | 62 | (22.1) | ||||
| Moderate | 142 | (50.7) | ||||
| High | 76 | (27.1) | ||||
| Exercise | ||||||
| Yes | 118 | (42.1) | ||||
| No | 162 | (57.9) | ||||
| Exercise duration | ||||||
| Short | 24 | (8.6) | ||||
| Moderate | 54 | (19.3) | ||||
| Long | 36 | (12.9) | ||||
| Sleep duration | ||||||
| Short | 24 | (8.6) | ||||
| Adequate | 222 | (79.3) | ||||
| Excessive | 34 | (12.1) |
SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI-body mass index; BP: blood pressure; RBG: random blood glucose
Table 2.
Gender-based comparisons
| Risk factor | Subjects | Mean±SD | P* | |||
|---|---|---|---|---|---|---|
| Height (cm) | Male | 175.35±0.58 | <0.001 | |||
| Female | 161.90±0.38 | |||||
| Weight (kg) | Male | 76.55±1.71 | <0.001 | |||
| Female | 59.94±0.77 | |||||
| BMI (kg/m2) | Male | 24.86±0.52 | 0.001 | |||
| Female | 22.89±0.29 | |||||
| SBP (mmHg) | Male | 123.35±1.48 | <0.001 | |||
| Female | 110.84±0.93 | |||||
| DBP (mmHg) | Male | 81.83±1.08 | <0.001 | |||
| Female | 72.49±0.86 | |||||
| BP (mmHg) | Male | 124.17±1.48 | <0.001 | |||
| Female | 111.56±0.93 | |||||
| RBG (mg/dL) | Male | 104.18±2.50 | 0.943 | |||
| Female | 103.97±1.51 | |||||
| Meal consumption | Male | 3.00±0.09 | <0.001 | |||
| Female | 2.67±0.04 | |||||
| Water intake | Male | 10.60±0.82 | <0.001 | |||
| Female | 6.57±0.21 | |||||
| Fast food consumption | Male | 3.28±0.24 | 0.002 | |||
| Female | 2.44±0.12 | |||||
| Exercise | Male | 3.73±0.43 | 0.990 | |||
| Female | 3.73±0.18 | |||||
| Sleep hours | Male | 6.75±0.20 | 0.042 | |||
| Female | 7.20±0.09 |
*P value is calculated using the Mann–Whitney U test
Obese students had the highest median systolic (126 mmHg) and diastolic (80 mmHg) pressures, while underweight students had the lowest. Short sleep duration was strongly associated with obesity (41.7%, P < 0.001). Fast food consumption was paradoxically more common among normal-weight individuals, while smoking was associated with overweight and obesity. BMI showed significant positive correlations with SBP (r = 0.30, P < 0.001) and DBP (r = 0.22, P < 0.001), but not with RBG (r = 0.05, P = 0.399) [Figure 2].
Figure 2.
The scatter plot of the Spearman’s correlation analysis between SBP (a), DBP (b), RBG (c), and BMI (a-c)
Obesity was more prevalent among males (15%), while underweight status was more common among females (12%) (P = 0.030). Fast food consumption was paradoxically more common among normal-weight students (P = 0.015). Smoking was linked with increased overweight and obesity, and short sleep duration strongly correlated with obesity (41.7%) (P = 0.053). Underweight students were more likely to be hypotensive, and obese individuals showed high rates of hypertension (83.3%). Exercise did not show a significant relationship with BMI [Table 3] (P < 0.001).
Table 3.
BMI associations with lifestyle and health factors
| Factors | Medical students, n=280 | x 2 | P* | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Level of BMI | ||||||||||||
| Underweight ≤18.4 n (%) n=28 | Normal 18.5–24.9 n (%) n=66 | Overweight 25.0–29.9 n (%) n=62 | Obese ≥30.0 n (%) n=24 | |||||||||
| Gender | ||||||||||||
| Male | 4 (5.0) | 44 (55.0) | 20 (25.0) | 12 (15.0) | 8.960 | 0.030 | ||||||
| Female | 24 (12.0) | 122 (61.0) | 42 (21.0) | 12 (6.0) | ||||||||
| Fast food consumption | ||||||||||||
| Yes | 24 (10.3) | 146 (62.9) | 44 (19.0) | 18 (7.8) | 10.408 | 0.015 | ||||||
| No | 4 (8.3) | 20 (41.7) | 18 (37.5) | 6 (12.5) | ||||||||
| Exercise | ||||||||||||
| Yes | 16 (13.6) | 64 (54.2) | 28 (23.7) | 10 (8.5) | 3.694 | 0.296 | ||||||
| No | 12 (7.4) | 102 (63.0) | 34 (21.0) | 14 (8.6) | ||||||||
| Smoking behavior | ||||||||||||
| Yes | 2 (6.3) | 14 (43.8) | 10 (31.3) | 6 (18.6) | 7.699 | 0.053 | ||||||
| No | 26 (10.5) | 152 (61.3) | 52 (21.0) | 18 (7.3) | ||||||||
| Sleep hours | ||||||||||||
| Short | 4 (16.7) | 8 (33.3) | 2 (8.3) | 10 (41.7) | 42.300 | <0.001 | ||||||
| Adequate | 22 (9.9) | 138 (62.2) | 52 (23.4) | 10 (4.5) | ||||||||
| Excessive | 2 (5.9) | 20 (58.8) | 8 (23.5) | 4 (11.8) | ||||||||
| BP | ||||||||||||
| Hypotensive | 8 (66.7) | 2 (16.7) | 2 (16.7) | 0 (0.0) | 53.862 | <0.001 | ||||||
P value is calculated using the Chi-square test
Obese students had the highest median systolic (126 mmHg) and diastolic (80 mmHg) blood pressures, while underweight students had the lowest. These differences were statistically significant (P < 0.001). However, while median random blood glucose levels were slightly higher in overweight and obese groups, the differences were not statistically significant (P = 0.125) [Table 4].
Table 4.
BMI and blood pressure/glucose differences
| Characteristics | BMI (kg/m2) groups | Median, interquartile ranges (IQR) | P by ANOVA* | LSD groups | P LSD | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| SBP (mmHg) | Underweight | 110 (99–120) | <0.001 | A X B A X C A X D B XC B X D C X D |
0.052 0.012 <0.001 0.653 0.007 0.118 |
|||||
| Normal | 115 (107–120) | |||||||||
| Overweight | 110 (110–120) | |||||||||
| Obese | 126 (110.3–133) | |||||||||
| DBP (mmHg) | Underweight | 62 (56–70) | <0.001 | A X B A X C A X D B XC BX D C X D |
<0.001 <0.001 <0.001 0.164 0.597 0.076 |
|||||
| Normal | 78 (70–80) | |||||||||
| Overweight | 73 (66–80) | |||||||||
| Obese | 80 (74.25–86) | |||||||||
| RBG (mg/dL) | Underweight | 94 (85–109) | 0.125 | A X B A X C A X D B XC BX D C X D |
0.108 0.248 0960 >0.999 >0.999 >0.999 |
|||||
| Normal | 104 (88–123) | |||||||||
| Overweight | 105 (87–122) | |||||||||
| Obese | 107 (85–124) |
*P value is calculated using Kruskal–Wallis one-way ANOVA test
Discussion
These findings are consistent with data from regions such as Karachi, Pakistan, where 33.2% of medical students were found to be overweight or obese.[6] In contrast, obesity prevalence in South India was 7.6%.[7] Differences in these figures may be due to varying lifestyles, geographical locations, assessment tools, or whether students lived in dormitories or at home.[8]
This study identified a high prevalence of both overweight/obesity and hypertension among medical students in Kurdistan, raising concerns about early cardiometabolic risks in a young and educated population. The prevalence of overweight and obesity (30.7% combined) in our cohort is concerning and aligns with trends observed in other regional studies. For instance, a study among medical students in Saudi Arabia reported a similar combined prevalence of 31.5%,[9] while a study in Malaysia found a rate of 29.6%.[10] This suggests that the shift towards a more sedentary lifestyle and energy-dense diet is a widespread issue in rapidly developing regions.
However, the most striking finding was the hypertension prevalence of 56.4%, which is markedly higher than rates reported in many similar demographic groups. For example, studies among medical students in the Philippines (14.5%),[11] India (22.4%),[12] and Ethiopia (19.3%)[13] report substantially lower figures. Even a large study of urban Indian adolescents reported a rate of only 6.4%.[14] This disparity suggests unique contextual, genetic, or stress-related influences in our cohort. The demanding academic environment, coupled with the specific socio-economic transition in the Kurdistan Region, may create a potent mix of chronic stress and lifestyle changes that disproportionately elevate blood pressure.[8]
Observationally, male students showed a strong preference for unhealthy foods like burgers, pizza, and sugary drinks, which likely contributes to higher obesity rates and increased stress compared to females. This pattern aligns with prior studies emphasizing the role of poor nutrition in weight gain.[15,16] Meanwhile, female students reported better sleep quality, which may help explain their generally lower blood pressure readings.[17]
The observed gender disparities are consistent with the global literature. Male students in our study had significantly higher BMI, SBP, and DBP than females. This pattern has been consistently reported in earlier studies.[16,18] The physiological underpinning for this is often attributed to the protective vascular role of estrogen in pre-menopausal women,[19] while behavioral factors are also significant. Our finding that males reported higher fast-food consumption is a critical behavioral correlate, supporting research that links poor dietary habits directly to adiposity and hypertension.[18] Conversely, the higher rate of underweight among females (12%) echoes findings from other studies in South Asia and the Middle East, often linked to body image concerns and sociocultural pressures.[20] Estrogen may provide a protective effect for females, while males in our study reported poorer diets and higher fast food intake, which may contribute to higher BMI and blood pressure.[18]
A core finding of our study is the strong positive correlation between BMI and both SBP (r = 0.30) and DBP (r = 0.22). This dose-response relationship is a well-established phenomenon in nutritional epidemiology. Our results are consistent with large-scale longitudinal studies, such as the Johns Hopkins Precursors Study, which identified BMI as a significant predictor of incident hypertension later in life.[16,18] The pathophysiological mechanisms linking obesity to hypertension are multifactorial, including activation of the sympathetic nervous system, insulin resistance, renal dysfunction, and chronic low-grade inflammation.[19] The absence of a significant correlation between BMI and RBG in our relatively young and likely pre-diabetic population is not unexpected, as dysglycemia often manifests later in the progression of metabolic syndrome.[18]
Our findings also revealed a strong positive association between BMI and BP in both sexes: as BMI increased, so did SBP and DBP. This trend aligns with previous studies[21,22] identifying BMI as a reliable predictor of elevated blood pressure.[18] Conversely, lower BMI was more commonly associated with hypotension.[18,21] These relationships may be explained by obesity-driven mechanisms such as insulin resistance, inflammation, and lipid imbalances, all of which contribute to vascular dysfunction and hypertension.[23,24]
Our analysis of lifestyle factors yielded nuanced insights. The paradoxical finding that fast food consumption was more common among normal-weight individuals has been observed in other settings. Research by Just and Wansink[25] suggests that for a majority of the population, occasional intake of “junk food” may not directly translate to weight gain, with total caloric intake and physical activity being more critical determinants. Furthermore, the strong association between short sleep duration and obesity (41.7%, P < 0.001) reinforces a growing body of evidence. Inadequate sleep disrupts the hormones leptin and ghrelin, increasing appetite and cravings for high-calorie foods, as documented in studies of young adults.[26]
The high prevalence of hypertension in medical students underscores the dual impact of lifestyle and academic stress. While most participants reported adequate sleep and balanced diets, hidden contributors such as stress, sedentary behavior, and irregular routines may drive cardiometabolic risk. Notably, RBG levels did not significantly differ across BMI groups, possibly indicating a lack of pronounced metabolic disruption in this relatively young and healthy population.[27] These findings highlight the urgent need for early preventive interventions and structured wellness programs in medical schools.
Limitations
This study’s limitations include its cross-sectional design, which prevents causal inference, and the use of convenience sampling, which may affect generalizability. The reliance on self-reported data for lifestyle factors introduces potential for bias. Despite this, a key strength is the objective clinical measurement of key variables like BP and BMI. As one of the first comprehensive studies in this population, it provides valuable, context-specific evidence of high hypertension rates, crucial for informing institutional health strategies.
Conclusion
This study reveals a dual burden of undernutrition and obesity among Kurdish medical students, coupled with alarmingly high rates of hypertension. These findings fall short of WHO health policy benchmarks, which recommend a normal BMI and blood pressure below 120/80 mmHg in young adults. Male students were particularly vulnerable to obesity and hypertension, while female students were more likely to be underweight. The positive association between BMI and BP emphasizes the importance of early detection and prevention strategies.
Targeted lifestyle interventions, stress management programs, and institutional policies that prioritize student health literacy and routine screening are urgently needed to safeguard the health of future healthcare providers.
Future studies should employ longitudinal designs to track the progression of risk factors. Qualitative research is needed to explore perceived barriers to healthy lifestyles. The effectiveness of targeted wellness interventions within the medical curriculum should be evaluated. Finally, expanding this research to include other universities and more comprehensive metabolic panels would be valuable.
AI policy statement
The authors declare that no artificial intelligence (AI) or AI-assisted tools were used in the research, writing, or editing of this manuscript.
Ethical approval
Ethical approval for the study was granted by the Ethics Committee at the College of Health Sciences, Hawler Medical University, under document number Sc.E.C.6B on 2 April 2024.
Author contributions
All authors critically reviewed and approved the final manuscript. They take full responsibility for its content, similarity index, and individual contributions, including conceptualization, project administration, supervision, data analysis, validation, original drafting, and editing.
Data availability statement
The data are available from the corresponding author upon reasonable request.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
The authors are thankful to all the participants of the study.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data are available from the corresponding author upon reasonable request.