Anemia and Cardiometabolic Risk in Indian University Students: Findings from a Health Screening Program

Abstract

Background

College students represent a population that is considered clinically at low risk; yet, early adulthood constitutes a crucial time point when nutritional and metabolic risks manifest themselves. This retrospective observational study evaluated anemia, blood pressure, and body mass index (BMI) characteristics of college students undergoing a health screening examination organized by their university in India.

Methods

Anonymized electronic health records were analyzed for 13,628 college students between the ages of 18 and 30 years old attending annual health screening from 1 June 2023 to 31 May 2024. The sample consisted of 77.2% of the eligible students. BMI, blood pressure, and hemoglobin levels were classified according to international standards. Gender-based differences were explored through chi-square tests and Cramer’s V measures. Association between anemia and screening-confirmed hypertension was assessed using Pearson correlations and multiple logistic regression. In addition, model discrimination, calibration, classification parameters, and internal validation were evaluated.

Results

There were 7,012 (51.5%) female students and 6,616 (48.5%) male students in the sample population. In total, 47.3% of the students were either overweight or obese, 8,081 (59.3%) fulfilled the criteria for high blood pressure according to the ACC/AHA guidelines, while 7,227 (53.0%) fulfilled WHO guidelines for anemia based on gender. Hypertension screening was also more common among men (66.6%) compared to women (52.4%; p < 0.001). In the adjusted models, being male was significantly linked with anemia and hypertension screening, while being overweight was significantly linked with hypertension screening.

Conclusion

This study showed the high prevalence of undiagnosed anemia and cardiometabolic risks among university students, suggesting the importance of health promoting universities in systematic screening programs and follow-ups.

Introduction

Higher education constitutes a significant development phase in the lives of young adults. The habits formed at this stage determine health trajectories throughout the lifespan, such as eating behavior, exercise, sleep, stress management, and usage of preventive care services [1–4] During their entry into higher education, young individuals experience greater freedom, academic challenges, changes in food environment, and lower levels of physical activities.

Noncommunicable diseases (NCDs) are still one of the major causes of morbidity and mortality globally, with the majority of risk factors being observed in adolescents and young adults [3, 5]. The case with India is that this stage of life coincides with a fast epidemiological shift where anemic prevalence remains high in combination with the growing rates of overweight, obesity, and high blood pressure [6, 7]. These factors do not always manifest at early stages of development; therefore, the need to screen arises.

The university context offers a unique opportunity for prevention on a population level. According to the framework of the Health Promoting University (HPU), health is understood not only in terms of personal behaviors but also organizational culture, environments, and policies [8]. In the same vein, the concept of Healthy Campus promotes the integration of prevention services, evidence-based approaches, and positive environments within universities [9]. Findings from university-based health screening programs point to the ability of these approaches to identify risks for poor health prior to their clinical manifestation [10–13].

In spite of an increasing body of knowledge regarding students’ health in the international arena, it is still hard to find any data that comes from the Indian higher education system and relates to large-scale, comprehensive, and almost universal screening within the university institutions [14–16]. The research carried out in the Indian higher educational institutions has focused on one disease at a time, involved volunteering on the part of respondents, and suffered from sample selection bias.

This research sought to review the results of mandatory annual health screening for all students enrolled in a large private higher education institute located in India. The purposes included estimation of prevalence rates of anemia, abnormal BMI, and hypertension among students; analysis of differences in these disorders between genders; investigation of correlation between BMI, age, and blood pressure levels; and generation of data necessary for development of the HPU-based health-promotion strategy.

Methods

Reporting Standards

This study has been carried out according to the guidelines provided by Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Study Design and Setting

The current study employed data extracted from the anonymized electronic health records collected via the annual health check-up process conducted by a private multi-campus university in India. The study period was from 1 June 2023 to 31 May 2024. The university health system provides primary healthcare and preventive services to students across constituent campuses through a centralized electronic health record platform.

The eligible student population during the study period was 17,656. The final analytical sample included 13,628 students with complete core data for age, sex, BMI, blood pressure, and hemoglobin, representing 77.2% coverage of the eligible population. No formal a priori sample-size calculation was performed because all eligible consecutive records available during the defined period were included. This consecutive EHR-based sampling approach improves institutional coverage but does not remove limitations related to generalizability beyond the study setting.

Study Population

The participants for the analysis comprised undergraduates and graduates aged 18 to 30 years. All data was eligible as long as all essential variables necessary for the categorization of either anemia, blood pressure, or body mass index were not missing.

Sources of Data and Variables

Anonymized data was sourced from the EHR database of the university. The following variables were included in the data analysis: age (years), sex (male/female), screening site on campus, height (centimeters), weight (kilograms), body mass index (kg/m2), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), hemoglobin levels (g/dL), and clinical advice or referrals. This rewrite changes the previous list format to a sentence structure in compliance with the reviewer’s instruction against using bullets wherever possible.

Outcome Measures and Standardization

Body Mass Index was grouped based on the categories by World Health Organization as underweight (< 18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity ( > = 30.0 kg/m2). The patient’s blood pressure was classified according to the classification by ACC/AHA into normal blood pressure (less than 120/less than 80 mmHg), high normal blood pressure (120–129/less than 80 mmHg), stage 1 of high blood pressure (130–139 mmHg. Hypertension in this study is considered as a result of being screened for being at high risk of having high blood pressure. However, hypertension cannot be considered a diagnosis as there were no repeated confirmatory tests done.

The height and weight were determined by means of a stadiometer and weighing scales in the absence of footwear while the subject was wearing light clothes. The BP was determined using the mercury-free manual sphygmomanometer after 5 min of resting on a seat. In case the first reading was ≥ 130/80 mmHg, the second measurement was made, and the average was noted. The hemoglobin was analyzed using venous blood samples and the cyanmethemoglobin method. For anemia, the criteria set by WHO were considered, which include < 12.0 g/dL hemoglobin for females and < 13.0 g/dL hemoglobin for males [17].

Statistical Analysis

Frequencies, percentage, mean, and standard deviation were obtained for descriptive statistics. Differences based on sex regarding anemia and screening-defined hypertension were assessed using Pearson’s chi-square test, with effect size given by Cramer’s V statistic. Correlation coefficients based on Pearson’s approach were calculated to determine the associations between body mass index (BMI), age, systolic BP, and diastolic BP. Adjusted odds ratio (aOR) and 95% confidence interval (CI) were computed by multivariate logistic regression for the associations between anemia and screening-diagnosed hypertension, including sex, age, and body mass index (BMI) as covariates and female sex as the referent group. Performance evaluation of the model was done using receiver operating characteristic (ROC) analysis, area under the curve (AUC), accuracy, sensitivity, specificity, Hosmer-Lemeshow goodness-of-fit test, and internal 5-fold cross-validation. Significance level was considered at p < 0.05.

Ethical Aspects

This study strictly followed the guidelines of the Helsinki Declaration, which were endorsed by the Institutional Ethics Committee at Symbiosis International (Deemed University) (Ref. No. SIU/IEC/1137). Individual consent from each patient was not required since the data used for analysis was de-identified and retrieved retrospectively from the medical records. Confidentiality of the information was ensured as the data could be accessed only by the investigators of the study.

Results

Participant Characteristics

The sample consisted of 13,628 students who underwent an annual health survey in the study period and had core data. The number of females was 7,012 (51.5%), while the number of males was 6,616 (48.5%). The average age was 21.1 years (SD = 2.7), compared to females’ 20.9 years (SD = 2.5) and males’ 21.3 years (SD = 2.9) for males.

The Distribution of BMI Categories, Blood Pressure Categories, and Anemia

WHO Classification of BMI indicated that 49.1% of students were within the normal BMI range, whereas 47.3% were either overweight or obese. Underweight condition was found among 3.5% of the students (Table 1).

Table 1.

Prevalence of BMI categories among university students (N = 13,628)

Category (WHO)	Female (n)	Male (n)	Total n (%)
Underweight	359	124	483 (3.5%)
Normal weight	4,220	2,473	6,693 (49.1%)
Overweight	1,767	2,169	3,936 (28.9%)
Obesity ( > = 30.0 kg/m²)	666	1,850	2,516 (18.5%)

In relation to blood pressure, there were 3,014 students (22.1%) who had normal results, while 2,533 students (18.6%) were at an elevated blood pressure. There were also 5,332 students (39.1%) that qualified for stage 1 while 2,749 students (20.2%) qualified for stage 2. Overall, 8,081 students (59.3%) met ACC/AHA screening criteria for hypertension. These findings should be interpreted as screening-based blood pressure risk rather than definitive clinical hypertension.

Applying WHO sex-specific hemoglobin cut-offs, 7,227 students (53.0%) were classified as anemic. This value corrects the previous manuscript count by one record after direct verification against the uploaded primary dataset.

Differences Based on Sex Strata in Health Risks

Significant differences according to sex were found for anemia and screening-diagnosed high blood pressure (Table 2). Males had more cases of anemia than females using the WHO criteria (67.4% versus 39.5%; chi-square(1) = 1066.6, p < 0.001; Cramer’s V = 0.280). The finding is biologically rare and is treated with caution because it may be influenced by sex-related hemoglobin cut-off levels and screening of anemia among men.

Table 2.

Prevalence of risk factors stratified by gender

Variable	Total (N = 13,628)	Female (n = 7,012)	Male (n = 6,616)	p-value
Anemia (WHO criteria)	7,227 (53.0%)	2,767 (39.5%)	4,460 (67.4%)	< 0.001
Screening hypertension (total)	8,081 (59.3%)	3,677 (52.4%)	4,404 (66.6%)	< 0.001
Stage 1 hypertension	5,332 (39.1%)	2,691 (38.4%)	2,641 (39.9%)	0.068
Stage 2 hypertension	2,749 (20.2%)	986 (14.1%)	1,763 (26.6%)	< 0.001

Anemia is defined as hemoglobin levels below 12.0 g/dL for women and 13.0 g/dL for men. Hypertension screening includes individuals meeting the criteria for either stage 1 or stage 2 hypertension according to the ACC/AHA classification system

Furthermore, hypertension detected through screening was significantly higher among men than women (66.6% and 52.4%, respectively; chi-square(1) = 280.9, p < 0.001; Cramer’s V = 0.144). Stage 2 hypertension occurred much more frequently among males than women, while stage 1 percentages were similar across genders.

Correlation Between Cardiometabolic Parameters

Results of Pearson correlation analysis revealed significant but weak correlation between the anthropometric and hemodynamic variables. There is positive correlation between body mass index and systolic blood pressure (r = 0.186, p < 0.001) and diastolic blood pressure (r = 0.213, p < 0.001). There is a weak correlation between age and systolic blood pressure (r = 0.044, p < 0.001) and diastolic blood pressure (r = 0.018, p = 0.033).

Multivariate Analysis and Model Performance

Adjusted odds ratios were obtained by fitting logistic regression models with variables such as gender, age, and BMI. Females served as the reference category. These models were intended for risk estimation purposes only and not for predicting clinical outcomes at the individual level (Table 3).

Table 3.

Logistic regression models for anemia and screening hypertension

Outcome	Predictor	aOR	95% CI	p-value
Anemia	Male (vs. Female)	3.20	2.97–3.45	< 0.001
	Age (per year)	0.99	0.98-1.00	0.166
	BMI (per kg/m²)	1.00	0.99–1.01	0.690
Screening hypertension	Male (vs. Female)	1.51	1.40–1.62	< 0.001
	Age (per year)	0.99	0.98–1.01	0.424
	BMI (per kg/m²)	1.07	1.06–1.07	< 0.001

In adjusted models, male sex was independently associated with anemia (aOR = 3.20, 95% CI 2.97–3.45) and screening-defined hypertension (aOR = 1.51, 95% CI 1.40–1.62). BMI was independently associated with screening-defined hypertension (aOR = 1.07 per kg/m², 95% CI 1.06–1.07), whereas age was not independently associated with either outcome in the adjusted models.

Discrimination for both models was moderate for anemia (AUC = 0.643) and screening defined hypertension (AUC = 0.616). This reinforces the idea that these models need to be considered explanatory models and not used as predictive instruments for clinical practice (Table 4).

Table 4.

Model Performance and Internal Validation

Outcome	AUC	Accuracy	Sensitivity	Specificity	Hosmer-Lemeshow p	5-fold CV AUC
Anemia	0.643	0.639	0.617	0.663	< 0.001	0.642 ± 0.018
Screening hypertension	0.616	0.609	0.833	0.282	0.476	0.615 ± 0.015

Clinical Consultations and Referrals

Clinical consultation and referrals were recorded in the EHR after screening. Students who had stage 2 blood pressure values (2,749; 20.2%) required immediate consultation and referral. Students classified as anemic (7,227; 53.0%) needed follow up consultation at the university health facilities.

The results presented in Fig. 1 reveal that males were more likely to suffer from anemia and screening defined hypertension than females. The result should be understood in light of sex-based criteria for anemia and screening nature of blood pressure evaluation.

Fig. 1.

Prevalence of anemia and screening defined hypertension by sex

Figure 2 It can be seen from Fig. 2 that almost 50% of the population had BMI levels beyond the normal, where 28.9% were obese while 18.5% were overweight.

Fig. 2.

Prevalence of body mass index category among university students

From Fig. 3, the ROC curves of the anemia and screening-defined hypertension models are shown above the reference diagonal line with moderate discrimination capacity.

Fig. 3.

ROC curve for logistic regression model

Discussion

This large EHR study conducted by him on annual health screening among university students in India revealed the presence of a significant problem of anemia, hypertension screening, and overweight/obesity in the young adults. It included data from 77.2% of the potential population among the university students and is hence an institutionally strong surveillance system. From within the Health Promoting University approach, these findings have confirmed that universities can be considered preventive environments that identify asymptomatic risk factors and institute appropriate follow-ups [8, 9, 13].

However, the anemia prevalence in this group was quite high at 53.0% among the students examined. On the other hand, anemia prevalence in India has been seen to be higher among reproductive-aged females [18] as compared to men, contrary to what has been witnessed in this particular group under investigation. Using sex-specific WHO cutoffs (< 13.0 g/dL for men and < 12.0 g/dL for women), a substantial number of mildly anemic young men can be identified who would not usually come to light with symptoms-driven medicine. As information on dietary intake, socioeconomic condition, menstrual status, ferritin levels, inflammation status, and hemoglobinopathies was unavailable, no causal explanation can be provided for this sex difference in the current investigation [17, 19, 20].

Nonetheless, such results highlight that the condition of anemia is no longer supposed to be seen as a strictly women’s issue within the health system of campuses. Indian universities have already used screening programs before to show that anemia and hemoglobinopathies can be detected among students, which may serve as a good base for prevention [14]. In this study, we have provided more data from EHRs to extend the current evidence in addition to highlighting the importance of gender-based screening, confirmation, counseling, and referral.

Screening-based hypertensive prevalence was also important, with 59.3% classified as ACC/AHA stage 1 or 2 hypertension. The above number is not to be taken as hypertension prevalence since there are no multiple measures of blood pressure during different visits available. Using the ACC/AHA thresholds to categorize young adults as hypertensive remains a point of discussion owing to their ability to generate higher classification rates due to the sensitivity of the cut-offs used [21, 22]. Nonetheless, within the context of a preventative campus-health care model, the classification of stage 1 and stage 2 cases would be helpful.

The relationship between sex differences and blood pressure was consistent with previous evidence indicating that male youth have higher risk profiles than female youth in regard to cardiovascular health. BMI had an independent association with hypertension, reflecting previous associations between body fat, vascular resistance, neurohormonal control, and blood pressure [23, 24]. Although the relationship between BMI and blood pressure was significant, it was weak, showing that BMI contributes to risk, but does not explain blood pressure variation in this young cohort.

In terms of policy recommendations, the study findings advocate for moving from screening alone towards creating an integrative learning health system approach. Screening needs to be followed up with referral, counseling, follow-up, physical activity, and clinical outcomes management. The HPU approach would fit into this model, as it goes beyond advising students on their health but focuses on creating an environment that is healthy for them through ensuring availability of affordable nutrition, physical activity facilities, mental well-being, and informed health policies [8, 12, 13].

Practice-wise, regular surveillance through EHR can assist in recognizing student groups who need focused intervention measures, thereby eliminating inequalities in relation to voluntary screening. Universities need to put forward protocols for conducting further testing of anemia, repeating blood pressure measurements, conducting behavioral counseling sessions, and referring students to a general practitioner. These protocols need to be established in the context of patient confidentiality and autonomy.

Strengths and weaknesses.

One of the major strengths of this research includes the large sample size and wide coverage due to a systematic annual screening programme. Standardization of definitions for Body Mass Index, Blood Pressure, and Hemoglobin increased the validity for comparison across other similar studies. Sex stratification and reporting of effect sizes, multiple regression models, and model evaluation improved the study’s rigour.

A number of limitations warrant consideration. First, the retrospective and cross-sectional nature of this study precludes any causal interpretations. Second, the measures for blood pressure and hemoglobin were taken from the screening data and not from the repeated clinical tests at different points in time, which could mean that screening criteria for hypertension overestimated clinically defined hypertension. Third, the study data lacked information on dietary factors, exercise, sleep, stress, socio-economic factors, family history, menstrual history, ferritin level, inflammatory markers, and screening for hemoglobinopathies, which hindered the causal inference of anemia and cardiometabolic disease. Fourth, the research was performed at one single private multi-campused university, and the external validity of results to other Indian universities, public universities, or rural institutions of higher learning might therefore be restricted. Lastly, the study examined screening effectiveness and made referrals, but did not look at patient compliance or follow-through.

Conclusion

This study offers institutional-based evidence on the prevalence of anemia and cardiometabolic risk in university students in India based on yearly health screening data from a major higher educational institution. The findings refute the presumption that all university students are necessarily healthy and reveal that a significant proportion of students have undiagnosed nutrition and cardiometabolic risks.

Overall, 53.0% of students met WHO sex-specific anemia criteria, 59.3% met ACC/AHA screening criteria for hypertension, and 47.3% were overweight or obese. Sex-based differences were evident, particularly for anemia and stage 2 blood pressure readings. Although the cross-sectional design does not establish causality, these findings highlight the value of university-based screening at a life stage when prevention is feasible.

Within the Health Promoting University model, annual screening should be linked to longitudinal follow-up, health education, dietary and physical activity interventions, and data-driven governance. Future prospective studies should incorporate behavioral, dietary, psychosocial, biochemical, and follow-up outcome data to clarify causal pathways and evaluate the effectiveness of campus health promotion interventions.

Author Contributions

Conceptualization: R.Y, A.CData Curation: A.CFormal Analysis: A.CInvestigation: A.CWriting – Original Draft: A.CWriting – Review & Editing: R.YSupervision: R.YProject Administration: A.C.

Funding

Open access funding provided by Symbiosis International (Deemed University). No specific grant was received for this research from any funding agency in the public, commercial, or not-for-profit sectors.

Data Availability

The data supporting the conclusions of this research are not publicly available as they contain information that might breach the privacy of research participants. Data can be made available upon reasonable request from the corresponding author and with the Institutional Ethics Committee permission **.**.

Declarations

Ethical approval

Ethics approval was obtained from the Institutional Ethics Committee of Symbiosis International (Deemed University), Approval No. SIU/IEC/1137.

Consent to participate

The requirement for individual informed consent was waived by the Institutional Ethics Committee because the study used anonymized retrospective EHR data.

Competing interests

The authors declare no competing interests.