How a Novel Mathematical Method Reveals Hidden Health Patterns
Imagine a typical college campus: students rush between classes, spend hours in libraries, and frequently choose fast food for convenience. Now consider this alarming finding—a recent study of university students revealed that 44.3% of young women showed abnormally high body fat percentages, with 50.4% exhibiting dangerous waist-to-hip ratios associated with metabolic diseases 8 . These aren't just statistics; they represent a silent health crisis unfolding in our educational institutions.
of female university students have abnormally high body fat percentages
The global obesity epidemic has not spared university populations, where academic pressures, lifestyle changes, and poor dietary habits converge to create a perfect storm for weight gain and metabolic disturbances. Traditional research approaches often struggle to capture the complex relationships between different body composition factors—how muscle mass might relate to visceral fat, or how dietary preferences connect with physical activity levels.
Enter InterCriteria Analysis (ICA), a sophisticated mathematical approach that can untangle these complex connections. In this article, we'll explore how scientists are applying this cutting-edge method to decode the hidden patterns in student obesity data, potentially revolutionizing how we understand and combat this growing health concern among our youth.
When we discuss body composition, we're moving beyond the simplistic notion of body weight to examine what our bodies are actually made of. Body composition refers to the proportions of different components that make up the human body, primarily distinguishing between fat mass and fat-free mass (which includes muscles, bones, and other tissues) 1 .
This distinction is crucial because not all weight is created equal. Two students might have identical body weights and BMIs, but one could have dangerous levels of visceral fat while the other has healthy muscle mass. Excess fat, particularly visceral fat surrounding organs, is strongly associated with cardiovascular and metabolic risks, including insulin resistance, type 2 diabetes, and various cancers 1 .
Researchers use several technologies to peer inside the human body:
This popular method uses a weak electrical current to measure resistance as it travels through body tissues 1 .
Originally developed for bone density assessment, DXA can also measure whole-body composition with high precision 2 .
Known commercially as the Bod Pod, this method measures body volume by calculating air displacement 2 .
Each method has strengths and limitations, but BIA has become particularly popular in field studies due to its portability, non-invasiveness, and relatively low cost.
InterCriteria Analysis (ICA) represents a groundbreaking approach to examining complex datasets. Developed using the principles of intuitionistic fuzzy sets, ICA acts as a mathematical detective that can identify hidden relationships between different criteria that might not be apparent through conventional statistical methods 7 .
Tells us if two factors are related, but provides limited insight into the nature of that relationship.
Quantifies both the degree of relationship and the certainty of that relationship, even with multiple interconnected variables.
Traditional data analysis might tell us that two factors are related, but ICA goes further—it quantifies both the degree of relationship and the certainty of that relationship. It does this by evaluating pairs of criteria across multiple objects (in this case, students) and assigning two values: one representing the positive correlation (μ) and another representing the uncertainty or hesitation (π) in that relationship.
Think of it this way: if we're looking at the relationship between physical activity and body fat percentage, ICA wouldn't just tell us if they're related; it would tell us how strongly they're related and how confident we can be in that relationship, even when other factors like diet, genetics, and lifestyle are simultaneously at play.
This makes ICA particularly powerful for analyzing multifaceted health data, where numerous interconnected variables influence outcomes simultaneously. It can help researchers distinguish between coincidental patterns and meaningful relationships, potentially revealing unexpected connections that could lead to more effective interventions.
In a pioneering study conducted at the Medical College of University "Prof. Dr. Asen Zlatarov" in Burgas, researchers applied InterCriteria Analysis to body composition data collected from college students 7 . The study aimed to identify hidden relationships between various anthropometric measurements associated with overweight and obesity.
Researchers gathered comprehensive body composition measurements from participating students using bioelectrical impedance analysis.
The measurements were systematically organized into a matrix where rows represented individual students and columns represented different body composition parameters.
Researchers applied the ICA method to this data matrix, calculating intuitionistic fuzzy pairs between all criteria.
The resulting relationships were analyzed to identify which body composition parameters were most strongly linked.
The application of InterCriteria Analysis to student body composition data yielded fascinating insights into the obesity puzzle:
| Parameter Pair | Relationship Strength | Potential Health Implications |
|---|---|---|
| Waist-to-hip ratio & Visceral fat | Both indicators of metabolic risk | |
| Body fat percentage & Muscle mass | Sarcopenic obesity pattern | |
| BMI & Body fat percentage | Highlights BMI limitations |
First, the analysis revealed non-obvious relationships between certain body composition parameters. For instance, the relationship between waist-to-hip ratio (a marker of fat distribution) and other metabolic risk factors showed different patterns than researchers might have expected using traditional analysis methods 7 .
Second, ICA helped identify which clusters of measurements tended to move together consistently across the student population. This clustering effect suggests that certain body composition changes may occur in predictable patterns, even if those patterns aren't immediately obvious.
Perhaps most importantly, the analysis demonstrated that the relationship between BMI and actual body fat percentage is more complex than typically assumed. This finding aligns with other research showing that BMI does not always have explanatory power for assessing body weight, as it doesn't distinguish between fat and non-fat mass 8 .
When we examine the concrete data from student body composition studies, clear patterns emerge that underscore the seriousness of this health issue:
Normal BMI: 89.5%
High Skeletal Muscle Mass: 36.8%
Normal BMI: 77.9%
Abnormally High Body Fat %: 44.3%
Abnormal Waist-to-Hip Ratio: 50.4%
The discrepancy between BMI readings and actual body composition is particularly striking. While the majority of students fall within the "normal" BMI range, more detailed body composition analysis reveals that many have potentially risky levels of body fat or unfavorable fat distribution 8 .
This paradox highlights the limitation of relying solely on BMI for health assessments and explains why more sophisticated approaches like comprehensive body composition analysis—and novel data examination methods like ICA—are needed to truly understand student health status.
During exams, fat mass significantly increased from 25.43% to 28.79%, while muscle mass decreased from 39.70% to 36.20% .
Recent research has further demonstrated how quickly student body composition can change under stress. During exam periods, one study documented concerning shifts. This demonstrates how academic pressures can directly impact physical health through both physiological mechanisms and behavioral changes.
The findings from student body composition research connect to a much larger public health crisis. Current projections suggest that by 2035, more than 1.77 billion people worldwide will be overweight, with 1.53 billion affected by obesity 3 . This represents 54% of all adults globally, with a significant proportion in low- and middle-income countries.
Each 1 kg/m² increase in BMI corresponds to a 5-7% increase in heart failure incidence independent of other risk factors 3 .
GLP-1 receptor agonists like semaglutide have demonstrated impressive results, producing average weight reductions of 14.9% 3 .
The health consequences extend far beyond physical appearance. Obesity is unequivocally linked to cardiovascular disease. The condition contributes to numerous other serious health problems, including certain cancers, reproductive challenges, and reduced life expectancy.
Fortunately, recent advances in obesity treatment offer hope. These medications represent a significant advancement in obesity management, though lifestyle interventions remain fundamental.
The research on student populations takes on special importance when we consider that habits formed during university years often persist throughout adulthood. Early identification of at-risk individuals through sophisticated analysis of body composition data could enable timely interventions that alter long-term health trajectories.
The application of InterCriteria Analysis to body composition data represents an exciting frontier in our understanding of student obesity. By moving beyond conventional statistical approaches, ICA gives researchers a powerful tool to decode the complex relationships between different aspects of body composition, lifestyle factors, and health outcomes.
As we've seen, the body composition challenges facing college students are far more complex than simple weight measurements can capture. The disconnect between BMI classifications and actual body fat percentages, the concerning rates of abnormal waist-to-hip ratios among young women, and the dramatic changes in body composition during stressful exam periods all point to the need for more sophisticated assessment and analysis methods.
The promise of approaches like InterCriteria Analysis lies not just in better understanding the problem, but in developing more effective solutions. By identifying which body composition parameters are most strongly linked to negative health outcomes, and which lifestyle factors most significantly influence these parameters, researchers can help design targeted interventions that address the root causes of student obesity rather than just its symptoms.
As this field advances, we move closer to a future where personalized health recommendations based on comprehensive body composition analysis and sophisticated data interpretation become the norm—potentially reversing the alarming trends in student obesity and creating a healthier trajectory for future generations.