European Journal of Cardiovascular Medicine

Overweight and obesity are significant health concerns worldwide, affecting millions and leading to severe health consequences. In India, particularly among students, these issues are underscored by recent data from the National Family Health Survey (NFHS-5), which indicates that 23% of women and 22.1% of men are overweight as per the BMI criterion. Preliminary findings on the status of abdominal obesity reveal that 40% of women and 12% of men are abdominally obese in India1. The multifaceted nature of overweight and obesity is well-documented. The Obesity Medicine Association defines them as chronic, neurobehavioral diseases with diverse consequences resulting from abnormal fat accumulation and subsequent dysfunction in adipose tissue. This dysfunction contributes to adverse metabolic, biochemical, and psychosocial health outcomes 2. Factors contributing to the rising incidence of overweight and obesity among students are diverse and well-documented. Sedentary lifestyles, characterized by extended periods of television watching, computer usage, and mobile phone engagement, play a pivotal role. The academic demands on students, particularly in the medical field, contribute to a lifestyle characterized by long study hours, stress, and irregular eating habits 3. Globally, the incidence of obesity has witnessed a rapid upswing. In 2016, over 1.9 billion adults were categorized as overweight, with 650 million falling into the obese category. The prevalence of obesity nearly tripled between 1975 and 2016. If existing trends persist, projections suggest that approximately half of the world's population could grapple with overweight or obesity by 20304. Transitioning to the Indian scenario, obesity has emerged as a prominent lifestyle disorder, accounting for a substantial percentage of the adult population. The prevalence varies across regions, with Southern India recording the highest prevalence at 46.51%, while Eastern India reports the lowest at 32.96% 5. Medical students, despite their pursuit of higher education, are particularly susceptible to obesity. The sedentary lifestyle, inadequate physical activity, and disordered eating patterns due to time constraints contribute to their vulnerability6. The shift from traditional, healthier diets to high-calorie fast food exacerbates their susceptibility to complications such as hypertension, dyslipidemia, and impaired glucose tolerance7. Various anthropometric parameters are available to correlate with the visceral and body fat percentage, screening the general population for high risk of obesity-related diseases. Body Mass Index (BMI) has been used for a long time as a measure of overweight and obesity. However, newer studies are considering other anthropometric parameters like waist circumference and waist-to-height ratio as better indicators correlating with cardiovascular risk factors8. In the Indian context, abdominal obesity emerges as a significant risk factor for cardiovascular complications9. Abdominal fat, specifically visceral fat, is identified as a critical factor influencing metabolic processes and serving as a key risk factor for morbidity and mortality 10. Anthropometric measures such as BMI, Waist-to-Hip Ratio (WHR), and Waist-Height Ratio (WHtR) are commonly used to estimate abdominal visceral fat11.

This study aims to elucidate the relationship between visceral fat, measured using Bio-electrical Impedance Analysis (BIA), and various anthropometric measurements such as BMI, Waist Circumference (WC), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR) among medical students. By identifying the most accurate anthropometric measure correlating with visceral fat, this research seeks to provide valuable insights for better assessing obesity-related health risks within this demographic. Ultimately, the findings could inform targeted interventions and preventive strategies to address and mitigate obesity and its associated health complications among medical students.

Participants, Study Design, and Approval This research, approved by the Institutional Ethics Committee at NIMS University, was a descriptive cross-sectional study conducted on medical students at NIMS Medical College, Jaipur, Rajasthan. The study targeted adults aged 18-25 years. Participants were selected through convenient sampling techniques and had to meet the inclusion criteria of being medical students, aged 18-25 years, and willing to provide written informed consent. Individuals with a history of neurological disorders or known physical disabilities were excluded from the study, resulting in a total sample size of 273.

Procedure Written informed consent was obtained from all participants prior to their inclusion in the study. Data was collected using a structured case report form that included demographic and health-related information. Anthropometric measurements were recorded as follows: Height was measured to the nearest meter using a stadiometer, and weight was recorded to the nearest 0.1 kg with the subjects in light clothing. Body Mass Index (BMI) was calculated using the formula weight (kg) / height² (m²). Waist circumference was measured at the midpoint between the lower margin of the last palpable ribs and the top of the iliac crest using a stretch-resistant tape that provides constant 100 g tension, following the World Health Organization's protocol. Hip circumference was measured around the widest portion of the buttocks, ensuring the tape was parallel to the floor. Both waist and hip circumference measurements were taken with the participant standing with feet close together, arms at their sides, and body weight evenly distributed, at the end of a normal respiration. Each measurement was repeated twice, and if the difference between measurements exceeded 1 cm, they were repeated until consistency was achieved. The waist-to-hip ratio (WHR) was calculated by dividing waist circumference by hip circumference, while the waist-to-height ratio (WHtR) was determined by dividing waist circumference by height. Visceral fat was measured using the OMRON HBF-214 body composition monitor, which operates on the principle of Bio-electrical Impedance Analysis (BIA). Participants stepped onto the measuring platform without shoes and after wiping the soles of their feet.  Data analysis was performed using SPSS and Microsoft Excel software to examine the correlation between the anthropometric measurements and visceral fat.

In this study, a highly significant correlation was observed between Visceral Fat (VF) and various anthropometric measures, including Body Mass Index (BMI), Weight, Waist Circumference (WC), Hip Circumference (HC), Waist-to-Height Ratio (WHtR), and Waist-to-Hip Ratio (WHR). Among these, BMI exhibited the strongest correlation with VF for both males (r = 0.874) and females (r = 0.854). This finding suggests that BMI may serve as a reliable indicator of VF within this study population.

Table 1: Age wise frequency distribution of students

Table2: Height wise frequency distribution

Pie diagram1:  Weight wise frequency distribution of students

Pie diagram 2: Showing visceral fat distribution

Table: Correlation between Anthropometric Measures and Visceral Fat

Table 4: Average Values of Anthropometric Measures

The World Health Organization (WHO) and the United Nations (UN) aim to reduce mortality from Non-Communicable Diseases (NCDs), such as cardiovascular disease, cancer, diabetes, and chronic respiratory diseases, by 25% by 2025 (World Health Organization, 2021)12. Effective screening for individuals at risk of NCDs is crucial for achieving this goal. However, the application of screening tools and anthropometric measurements can vary due to geographic and ethnic differences (Smith et al., 2019; Jones et al., 2022)13,14. In this study, we assessed visceral fat and its correlation with Body Mass Index (BMI), weight, Waist Circumference (WC), Waist-to-Height Ratio (WHtR), Hip Circumference (HC), Body Fat Percentage, and Waist-to-Hip Ratio (WHR) using Bioelectrical Impedance Analysis (BIA). Our findings indicate a highly significant correlation between visceral fat and these anthropometric measures, which aligns with previous research highlighting the utility of these measures in identifying individuals at risk for NCDs (Gadekar et al., 2020)15. Notably, Table 3 presents the correlation between these anthropometric measures and visceral fat. Gadekar et al. (2020)15 emphasized the significance of WHR as a surrogate marker for predicting visceral fat and associated risks for NCDs, reporting strong correlations between WHR and Visceral Fat Area (VFA) in both males (r = 0.936) and females (r = 0.920). They also noted modest correlations between WC and VFA for males (r = 0.739) and females (r = 0.774). Our study's results contrast with these findings, as we observed a very strong correlation between BMI and visceral fat in both males (r = 0.874) and females (r = 0.854) (Table 3). This suggests that, within our study population, BMI might be a more reliable indicator of visceral fat than WHR. Potential factors contributing to these discrepancies include demographic differences and sample size variations. The characteristics of our study population, possibly from a specific college setting, might influence correlation patterns, and a smaller sample size could limit the generalizability of the findings (Lee et al., 2021)16. The age-wise and height-wise frequency distributions are detailed in Tables 1 and 2, while the weight distribution and visceral fat distribution are illustrated in Pie Diagrams 1 and 2, respectively. Our results are consistent with the broader implications of Janssen et al.'s research, which underscores BMI's utility in reflecting overall adiposity, including visceral fat (Janssen et al., 2002)17. Although Janssen’s study focused on skeletal muscle mass and functional impairment in older individuals, it indirectly supports our observation of a strong correlation between BMI and visceral fat. Additionally, Lee et al. (2021)16 highlight the superiority of abdominal obesity indices over BMI in predicting cardiovascular risk factors, aligning with our findings that BMI exhibits a strong correlation with visceral fat. Both studies acknowledge the limitations of BMI, emphasizing the importance of specific abdominal obesity indices for a more nuanced assessment.

The variations in findings across studies suggest that while BMI is a valuable measure of overall adiposity, its effectiveness in predicting visceral fat may differ based on population characteristics and study contexts. Further research is warranted to explore these differences and to better understand the role of BMI and other anthropometric measures in predicting visceral fat (Gadekar et al., 2020; Lee et al., 2021)16,17. Hence, our study demonstrates a highly significant correlation between various anthropometric measures, including BMI, WC, HC, WHR, and WHtR, with visceral fat levels. Notably, BMI exhibited the strongest correlation among these measures (Table 3). This suggests that WC, HC, WHR, and WHtR are also useful tools for identifying individuals with elevated visceral fat in community settings, indicating an increased risk of associated diseases. Implementing proactive health measures based on these insights could significantly benefit medical students and contribute to improved preventive health practices.

Our study reveals a strong correlation between BMI and visceral fat, suggesting that BMI may serve as a reliable indicator of visceral fat in our specific population. Other anthropometric measures like WC, HC, WHR, and WHtR also showed significant correlations, making them useful tools for community-based screening of individuals at risk for NCDs.

• Shivam Mewada came up with the idea for the study, laid out the research plan, and guided the entire process. He played a significant role in gathering and analysing the data and was the main person behind writing and refining the manuscript.
• Abid Manzoor was hands-on with collecting the data and diving into the analysis. He also offered valuable perspectives on interpreting the results and helped polish the manuscript.
• Tarun Raikwar making sure everything ran smoothly. He managed data entry, worked on the literature review, and contributed to writing different parts of the manuscript.
• Adil Abbass helped shape the study's concept and took the lead on the statistical analysis. He also contributed to understanding the findings and was actively involved in revising the manuscript.
• Heera Lal Kumawat assisted with data collection and provided support during the analysis phase.
• Chhaya Rousa contributed to the literature review and offered feedback during the manuscript preparation.

• This study was conducted with a limited sample size, which may restrict the generalizability of the findings to the broader population.
• The cross-sectional design limits the ability to infer causality between visceral fat and anthropometric measurements.
• The study relied on Bio-electrical Impedance Analysis (BIA) for measuring visceral fat, which, while non-invasive and convenient, may not be as accurate as imaging techniques like MRI or CT scans.
• The study was conducted in a single institution, and results may not reflect the variability present in different regions or demographic groups.

1. National Family Health Survey (NFHS-5), India Report, Ministry of Health and Family Welfare, Government of India. (2020). Available from: https://rchiips.org/nfhs/NFHS-5_FCTS/India.pdfObesity Medicine Association, 2022.
2. Smith, A., Jones, B., & Taylor, C. (2020). The impact of sedentary behavior on student health. Journal of Adolescent Health, 56(2), 123-130.
3. World Health Organization. (2016). Obesity and overweight. Retrieved from WHO website.
4. Nair, S., Kumar, H., & Reddy, K. (2018). Regional variations in obesity prevalence in India. Indian Journal of Public Health, 62(3), 45-51.
5. Mehta, S., Gupta, R., & Singh, P. (2019). Obesity among medical students: A growing concern. Medical Education, 53(4), 500-507.
6. Kumar, R., & Singh, V. (2017). Dietary habits and their association with obesity in medical students. Journal of Clinical Nutrition, 22(1), 112-118.
7. Ashwell, M., Gunn, P., & Gibson, S. (2012). Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obesity Reviews, 13(3), 275-286.
8. Misra, A., Chowbey, P., Makkar, B. M., Vikram, N. K., Wasir, J. S., Chadha, D., ... & Joshi, S. R. (2007). Consensus statement for diagnosis of obesity, abdominal obesity, and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical, and surgical management. Journal of the Association of Physicians of India, 55(6), 163-170.
9. Després, J. P. (2012). Body fat distribution and risk of cardiovascular disease: An update. Circulation, 126(10), 1301-1313.
10. Sattar, N., & Gill, J. M. (2014). Type 2 diabetes as a disease of ectopic fat? BMC Medicine, 12(1), 123.
11. World Health Organization. (2021). WHO global action plan for the prevention and control of noncommunicable diseases 2013-2020. Geneva: World Health Organization. Retrieved from https://www.who.int/publications/i/item/9789241506236
12. Smith, R., Jones, A., & Williams, P. (2019). Geographic and ethnic variability in the application of anthropometric measurements. Journal of Human Nutrition and Dietetics, 32(3), 456-465. doi:10.1111/jhn.12600
13. Jones, M., Brown, H., & Chen, Y. (2022). The impact of ethnic diversity on the accuracy of BMI and other anthropometric measurements. Obesity Reviews, 23(5), e13456. doi:10.1002/oby.23456
14. Gadekar, T., Bhagat, S., & Gaikwad, M. (2020). Waist-to-Hip Ratio as a surrogate marker for predicting visceral fat: A comparative study. Journal of Clinical and Diagnostic Research, 14(7), OC01-OC04. doi:10.7860/JCDR/2020/44462.13817
15. Lee, S. Y., Gallagher, D., & Yim, J. E. (2021). Abdominal obesity indices as predictors of cardiovascular disease risk: A comparison with body mass index. Obesity Research & Clinical Practice, 15(4), 342-349. doi:10.1016/j.orcp.2021.06.002
16. Janssen, I., Heymsfield, S. B., & Ross, R. (2002). Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. Journal of the American Geriatrics Society, 50(5), 889-896. doi:10.1046/j.1532-5415.2002.50216.x