Obesity is characterized by an excessive accumulation of fat that results in abnormal body weight gain. It is a critical global health issue, closely linked to cardiovascular diseases (CVD) and is a leading cause of both mortality and morbidity worldwide (1). Obesity typically arises due to an imbalance between caloric intake and energy expenditure, with energy intake surpassing energy output (2, 3). This condition is further complicated by various health challenges, such as vascular and metabolic dysfunctions, and is particularly concerning in children, adolescents, and young adults. Obesity contributes to the development of a multitude of morbidities, including stroke, coronary artery disease (CVD), dyslipidemia, type 2 diabetes, hypertension, gastroesophageal reflux disease (GERD), obstructive sleep apnea (OSA), osteoarthritis, insulin resistance, carpal tunnel syndrome, and even certain types of cancer (4-8).

Commonly employed methods for assessing obesity include body mass index (BMI), waist-to-hip ratio (WHR), and body fat percentage (%BF), often supplemented by monitoring blood glucose and cholesterol levels. Obesity continues to rise globally, with estimates suggesting that nearly 2 billion people will be classified as obese, and 13% of adults already fall into this category (9).

Biochemically, glucose is metabolised to fatty acids that form body fat level of BMI which causing increased lipid biosynthesis and also creates cholesterol; hence body weight will increase (10). Insulin is secreted from beta cells of the islets of Langerhans in the pancreas that binds to the specific cell receptor results in enhancing glucose uptake into the cell (11). Insulin is an anabolic hormone acts in energy conversation and hence suggests the body to produce fat. As body fat and overall weight increase, so does BMI. Increasing BMI level, insulin resistance are increasing as a result blood glucose level increased. As blood glucose increases, the cholesterol level also increases. Since, it may be expected that blood sugar level and cholesterol level are correlates by BMI level. While BMI is the most widely used metric for evaluating obesity, it remains a suboptimal tool for diagnosing CVD and metabolic disorders.

The measurement of waist and hip circumference can complement BMI, providing a more comprehensive assessment of CVD and metabolic disease risk. Excess visceral adipose tissue is particularly linked to CVD, and dysfunctional subcutaneous adipose tissue may lead to ectopic fat deposition, such as in the heart, liver, skeletal muscles, and pancreas (12). This visceral fat, often referred to as visceral obesity, plays a crucial role in CVD development and is influenced by body composition phenotypes (13,14). The distribution of body fat can also vary depending on age, sex, genetics, and environmental factors (15,16). Therefore, assessing body fat quantitatively is vital for identifying potential health risks, especially related to CVD and metabolic disorders (17). Consequently, cross-sectional studies indicate a strong association between obesity, diabetes, and the risk of CVD complications.

Aims & objectives:

The aims of this study are as follows: -

• It helps to evaluate unhealthy students which have overweight or obesity problems.
• This helps to identify diabetic and pre-diabetic students.
• This study helps to evaluate future probability of cardio-vascular risks.

Materials & methodology:

• Study design, settings and sample quantity:

The cross-sectional study was steered out at the students of Bhavan’s Tripura College of science and Technology (BTCST), Anandanagar, West Tripura from April to May 2025. Students aged from 19-23 years readily partake in this research work after expressing willingness. The processed willingness encircles the narrate of the medical history such as sex, age, socio-economic condition, anthropometric variables and collection of data for biochemical analysis.

• Anthropometric measurement:

By using standard techniques, the anthropometric measurements are taken; these include triceps skin thickness (TRSF), subscapular skin thickness (SBSF), height, body weight, waist circumference (WC) and hip circumference (HC).

   By wearing minimum clothing also with barefoot in the standing position of body weight and height were measured. For evaluated the height, a stadiometer was used. The head was held in the auriculo-orbital plane and the data were recorded to the nearest 0.1 cm. Weight was evaluated by means of a portable weighing machine and the data was recorded to the nearest 0.5 kg. For calculated body mass index (BMI) weight in kg was divided by square of height in meter, i.e., BMI = weight (kg) / height² (m²).

For waist and hip circumference, a tap meter was used while the subject in a standing position. The WC was precision-measured at the axial mid-point, equidistant from the lower rib cage and the iliac-crest, with the abdominal muscles relaxed and arms in a neutral position. The measurement was calibrated to the nearest 0.1cm. Conversely, the HC was assessed as its maximum girth, situated between the hip and gluteal prominences, and recorded to the nearest 0.1 cm. The waist-to-hip ratio (WHR) was subsequently calculated by dividing the WC by the HC, yielding a valuable waist-to-hip ratio.

Skinfold thickness, such as triceps and subscapular skinfolds, was precisely quantified using a Holtain skinfold calliper, which applied a consistent spring pressure of 10 g/mm², including the triceps and subscapular regions, on the right side of the body. Triplicate measurements were taken at each site, ensuring accuracy and reliability and the mean values were calculated to yield a single, representative data point. The process was repeated to obtain a comprehensive, standardized measurement providing a precise indicator of subcutaneous adiposity.

• Calculation of body fat percentage (%BF) was by using the sum of triceps and subscapular skinfold thickness to portend the following equation –

%BF = 1.33 (TRSF +SBSF) – 0.013 (TRSF +SBSF)² – 2.5

• Calculation of fat mass (FM) by body weight and %BF.

FM (kg) = Body Weight(kg) × %BF/100

• By using BMI and %BF the fat mass index (FMI) was calculated.

FMI (kg/m²) = %Bf × BMI/100

• Fat free mass (FFM) was calculated by following formula-

FFM (kg) = body weight × {1-(%BF/100)}

• Fat free mass index (FFMI) was calculated by FFM and height.

FFMI = FFM (kg) / height² (m²)

• Biochemical analysis:

Biochemical test was conducted through the precise quantification of glucose and cholesterol levels in blood sample.

Blood glucose levels were measured by utilizing glucose diagnostic kits in conjunction with a semi-automatic analyzer and BOD incubator. The enzymatic assay employed a glucose reagent, calibrated standards and serum specimens to facilitate an accurate and reliable measurement of glucose concentrations. A series of standardized glucose solution with predetermined concentrations were prepared, followed by the creation of a test sample mixture comprising 1ml of glucose reagent and 0.01ml of serum. This mixture was then incubated at 37^◦cfor 10 minutes, allowing the enzymatic reaction to reach optimal levels. After incubation the intensity of the colour solution is measured by an analyzer and calculate the glucose level in blood serum.

Utilizing diagnostic kits in conjunction with a semi-automatic analyser and BOD incubator. The enzymatic assay employed a cholesterol reagent, calibrated standards and serum specimens to facilitate an accurate and reliable measurement of cholesterol concentrations. A series of standardized cholesterol solutions with predetermined concentrations were prepared, following by the creation of a test sample mixture comprising 1ml of cholesterol reagent and 0.02ml of serum. This mixture was then incubated at 37^◦cfor 10 minutes, allowing the enzymatic reaction to reach optimal levels. Subsequently, an analyzer was employed to precisely measure the cholesterol levels in the blood sample, yielding a comprehensive cholesterol level.

• Statistical analysis:

A comprehensive excel spreadsheet was populated with detailed datasets, encompassing demographic information (name, age, sex) and an intensive array of physical and biochemical metrics. Advanced formulas were applied to facilitate intricate calculations, yielding mean and standard error values for each anthropometric variables, stratified by age and ethnicity. Categorical variables were elucidated through frequency distributions and percentage allocations. Subsequently, usable statistical analysis was conducted utilising the SPSS-19 statistical package, with a significant threshold set at P<0.05 to ensure the reliability of findings.

Result:

A total of 120 college students from three consecutive semesters participated in the study. The standard BMI values were categorized into three distinct groups: individuals with the BMI <18.5 were classified in the underweight, those with a BMI between 18.5-24.9 were considered to have a normal weight, while individuals with a BMI ranging from 25-29.9 were regarded overweight, and those with a BMI >30 were categorized as obese. Furthermore, the waist-hip ratio was classified into two groups: Non-obese (<0.85 cm) and obese (≥0.85 cm). A summary of the study variables is presented in table 1. It indicates that sex differences in body weight, height, Fat Mass Index, Fat Free Mass and Fat Free Mass Index are statistically significant (p value > 0.05). Table 1 represents the summary of study variables. As specially, a total of 120 participants, 59 females and 61 males, between 19 to 23 years of age were include in this study.

Table 1: Demographic, anthropometric and biochemical parameters in sample (n=120)

* Significant at p value <0.05

Table 2 represents the characteristics of visceral fat analysis of male and female students. According to body mass index 28.33% and 23.33% of female and male students are normal, respectively.

In the case of waist-hip ratio, girls (26.67%) are more obese than boys (25.83%).

Table 2: Characteristics of Visceral fat analysis (n=120)

BMI: Body Mass Index; WHR: waist to hip ratio.

Figure 1: Student’s mean cholesterol level (mg/dl)

Figure 2: Student’s mean blood glucose level (mg/dl)

Table3: Correlation of cholesterol and blood sugar with different variables of body composition