European Journal of Cardiovascular Medicine

The cardiovascular diseases are the primary cause of morbidity and death globally.[1] A significant increase in obesity rates has been linked to the accelerating increase in cardiovascular disease around the world. Excessive body fat accumulation is the hallmark of obesity, a metabolic illness that is closely linked to cardiovascular disease.[2] In a population, the most popular measure of weight is body mass index (BMI).  However, it cannot differentiate between lean mass and body fat. So, it is crucial to do a direct analysis of body composition instead of depending exclusively on the body mass index in order to determine lean body mass.[3]

The proportion of lean tissue to fat makes up the body composition. Accurate body fat measurements may offer clinically helpful recommendations for evaluating the risks of obese individuals and maximising therapeutic and preventative interventions.[4]

Numerous techniques, such as magnetic resonance spectroscopy, dual energy X-ray absorption, air displacement plethysmography, dilution procedures, and bioelectrical impedance analysis, can be used to evaluate body composition. An approach that is frequently used to estimate body composition is body composition analyser based on the principle of bioelectrical impedance analysis. A little electrical current's impedance or resistance as it passes through the body's water pool is measured by bioelectrical impedance analysis. Assuming that 73% of the body's fat-free mass is water, body composition analyser gives an estimate of total body water from which total body fat-free mass can be computed.[5]

It has been observed in various studies that body fat mass is an independent risk factor for ECG variations and cardiovascular events.[6] For clinical diagnosis, the heart's ventricular activity is very important. Usually, the systolic and diastolic characteristics determine the cardiac electrical activity. [7] A substantial layer of subcutaneous fat between the heart and the ECG electrodes weakens the cardiac electric potential before it reaches the electrode because fat is electrically resistive.[8] Consequently, an increase in adipose tissue lowers the ECG wave voltage, particularly for ventricular activity.[9]

Medical students have a vast curriculum leading to emotional and physical stress along with a more sedentary lifestyle, so medical students are at a higher risk of developing cardiovascular disorders. Prevalence of cardiovascular disorders among medical students has been shown in various studies. Therefore, it becomes essential to identify the high-risk individuals among them, as medical students have an inevitable role to play in public health.[10] So, the study was done to determine how body composition and heart ventricular activity in medical students relate to one another and also there are no similar studies done on medical students in Northern India.

Aim

Assessment of the correlation between body composition and ECG ventricular activity in medical students.

Objectives

• Identify the medical students having abnormal ventricular activity.

• Assess the correlation between body composition parameters and ventricular parameters.

After receiving approval from Ethics Committee for Biomedical & Health Research (AU/EC_BHR/2K24/524), a cross-sectional analytical study was conducted in the Department of Physiology. The study was conducted on 113 M.B.B.S students of the institute after calculating the sample size for correlation coefficient.[11] Among the 113 students 40 were male and 73 were female students. The written informed consent was taken from each participant.

Inclusion Criteria

1. Normal healthy medical students between the ages of 18 and 24 were included in the study.

Exclusion Criteria

1. Students who had history of diabetes, hypertension, mental health issues, metabolic abnormalities, or any other chronic illnesses.
2. Students who were on long-term medications.

Study Procedure

Each participant was explained about the study procedure and their detailed history was taken. Participants' weight and height were measured with minimal clothing and no shoes. A weighing scale was used to record weight in kilograms, and a stadiometer was used to record height in meters. Body mass index (BMI) of each participant was determined by using the formula [12]:

 BMI = Weight (Kg) Height (m)²

Table 1. Descriptive Statistics for Body Composition Variables.

Evaluation of Body Composition:

The assessment of body composition was done in morning before breakfast between 8 am to 10 am using Omron HBF-375 body composition analyser. Two readings of BFP were recorded and the average value was calculated.

Recording of ECG Parameters

Each participant's ECG was recorded following the body composition assessment. Every participant has been initially asked to lie down in supine position for 5 minutes. QT interval, RR interval and Heart Rate (HR) were determined from the ECG that was recorded. Also, TQ interval, corrected TQ interval (TQc) and corrected QT interval (QTc) were calculated. The electrical diastole and electrical systole ratios, TQ/QT and TQc/QTc, were calculated.  Additionally, the RR/TQ and RRc/TQc cardiac cycle to electrical diastole ratios were determined.  The time between two consecutive R waves was measured to calculate the RR interval.  The Tangent technique was used to calculate the QT interval.[13] The Bazett formula was used to calculate the QTc interval.[14]

QTc = QT/RR^1/2

All parameters were derived from Lead II of the ECG.

Since QT and QTc intervals represent ventricular electrical systole, the following formula was used to consider the TQ and TQc intervals as the equivalent of ventricular electrical diastole:[13]

TQ=RR-QT

TQc=RRc-QTc=1000-QTc

RRc stands for the standard value of 1000 ms in the final equation.[14]

Statistical Methods

Data was collected and analysed using Microsoft Excel 2007. Mean, standard deviation, minimum, and maximum descriptive statistics were calculated. Correlation analysis was done to assess associations between ECG ventricular parameters: “HR, QT, QTc, TQ, TQc, RR, TQ/QT, RR/TQ, TQc/QTc, RRc/TQc” and body composition measures (weight, BMI, BFP). Variables that showed significant associations were subsequently analysed using linear regression analysis.

There were 113 students in the study (mean age: 19.4 ± 1.14 years; 35.4% male, 64.6% female).  Table 1 depicts the body composition metrics (weight, BMI, and BFP). The mean BMI of the participants was 24.72 ± 4.81, indicating that the majority of the participants fell within the normal weight category, as per WHO classification.[15]According to WHO classification of BMI [15] , 1.76% of participants were classified as severely underweight, 7.07% as underweight, 51.32% as having normal weight, 28% as overweight, 7.07% as having Class I obesity, 3.53% as Class II obesity, and 0.88% as Class III obesity. Body fat percentage (BFP) was evaluated using age- and gender-specific reference standards[16,17] according to which 1.76% of participants were identified as having low BFP, 46.01% as having normal BFP, 34.51% as having high BFP, and 16.8% as having very high BFP.

Table 2. Descriptive Statistics for ECG Ventricular Parameters.

The statistical indicators for the ECG ventricular function parameters assessed in this study are depicted in Table 2.All participants showed sinus rhythm with no abnormal changes, and the mean heart rate was 83.42 ± 14.81 beats per minute. The corrected QT interval (QTc), which reflects the duration of ventricular repolarisation, is usually considered normal between 350 and 440 ms, although the reference values differ by sex.[18] For adult males, a QTc of less than 440 ms is regarded as normal, while for adult females, the normal limit is less than 460 ms.[19] In our participants, 99.11% had QTc intervals within these normal limits, whereas 0.88% had values above the normal range.

Table 3. Pearson's correlation coefficient (R) between the recorded variables and the degree of statistical significance.

*p-value less than 0.01 is significant

The association between body composition and ECG ventricular characteristics was shown by correlation analysis (Table 3).  Weight and BFP (R = 1, p < 0.01), QTc and BFP (R = 0.30, p < 0.01), TQc and BFP (R = -0.28, p < 0.01), and TQc/QTc and BFP (R = -0.35, p < 0.01) were correlated.

Table 4: Linear regression analysis between BFP and weight, QTc, TQc and TQc/QTc.

*p-value less than 0.01 is significant

Linear regression analysis confirmed significant associations between BFP and four key outcomes: weight, QTc, TQc, and TQc/QTc ratio. (Table 4) All models were statistically significant (p < 0.01), and the following regression equations were derived:

Weight = 0.522 × BFP + 53.294

QTc = 0.824 × BFP + 390.074

TQc = -0.893 × BFP + 612.792

TQc/QTc = -0.006 × BFP + 1.606

Statistical models of regression using BFP as the independent variable showed significant predictions for all dependent variables, including weight, QTc, TQc, and TQc/QTc

Our study shows a positive relationship between body fat percentage (BFP) as well as corrected QT interval (QTc), which indicates that as BFP increases, the QTc interval tends to lengthen. This result aligns with the research^[20]  conducted on 96 healthy males of age group between 20-39 years in Etmadpur, Agra, Uttar Pradesh which showed higher QT and QTc values in obese subjects as compared to normal subjects. The prolongation of the QTc interval may be  caused by the extended action potential in ventricular myocytes. The increase in QTc interval with increase in body fat percentage (BFP) in our study may be due to the fact that the conduction is slowed down  in the cardiac muscle due to increased amount of fatty tissue in the cardiac musculature. It has been found that obesity can lead to alterations in the structure and electrical characteristics of myocardial cells, even when there are no additional health issues or existing heart conditions. [20] The prolonged ventricular repolarisation, also known as an extended QT interval has been linked to obesity and hence increases the risk of ventricular arrhythmias and sudden cardiac death. Also, the cytokines derived from adipose tissue may aggravate QT prolongation by interfering with potassium channels in the myocardium. [21,22] The evaluation of QTc lengthening might serve as an indicator of higher mortality rates in healthy obese subjects, even if the QTc values themselves are not significantly elevated in these patients.[9]

Free fatty acids are linked to increased sympathetic activity of heart and abnormalities in repolarization [23]. A study ^[24] conducted in Naples, Italy on 70 obese women of the age group 25 to 44 years which reported that in obese subjects increase in QT and QTc intervals has been attributed  to autonomic dysfunction with sympathovagal imbalance.

The result of our study also shows negative correlation between the body fat percentage (BFP) and the corrected TQ (TQc) interval. This finding is similar to the  study by Iconaru and Ciucurel, 2022^[9] conducted on 50 healthy subjects of age group between 19-23 years. The decrease in TQc interval with increase in BFP was may be due to altered electrical diastolic function with increase in adiposity.[9] The QT interval represents systolic phase, of the cardiac cycle is influenced directly by the TQ interval, which is an indicator for the diastolic phase.

 The variations in the TQ interval dynamics can be used to explore the impact of stress on the diastolic function of the heart.[25] The electrical diastole to electrical systole ratio (TQc QTc) reflects  coordinated contraction as well as relaxation of the heart and serves as a non-invasive indicator of diastolic function.[24] In our study body fat percentage (BFP) and the electrical diastole to electrical systole ratio (TQc/QTc) showed negative correlation which suggests that an increase in fat mass leads to alterations in ventricular electrical activity which is similar to those induced by acute stress.[24]

A study by Sharafi et al.,2025^[21] conducted on 10213 participants of age group 35-70 years in Southern Iran showed that Total Body Fat Percentage (TBFP) is a more accurate predictor of cardiovascular events. Regardless of BMI, higher levels of total body fat percentage (TBFP) are linked to an increased risk of developing cardiovascular diseases.[21] In the present study body fat percentage (BFP) shows a stronger correlation with the ECG parameters than body mass index (BMI). The finding is consistent with the study done by Hatani et al., 2020^[26] conducted on 71 asymptomatic type 2 diabetes mellitus patients with preserved left ventricular ejection fraction which demonstrated that increased body fat mass was associated with reduced left ventricular longitudinal myocardial systolic function.

The electrical activity of the ventricles in young adults is influenced by body composition, signalling a risk for ventricular dysfunction in medical students with elevated body composition and indicating the need for early intervention. Furthermore, compared to body mass index (BMI), body fat percentage (BFP) provides a more accurate indicator of the risk for impaired ventricular function.

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