“obesity paradox” in medical settings.
The operational definition of obesity varies depending on the metrics and methods used by researchers to explore the widespread health issue. Purnell (2018) asserts that obesity is recognized and categorized as a non-communicable, chronic disease and that recent studies concerning the physiology of weight regulation terming the health issue as the pathophysiology that contributes to unwanted weight gain. Although previous studies have come up with an operational definition of obesity by agreeing that adults are considered obese when they have Body Mass Index (BMI) greater than 30, the classification system has failed to offer distinctive caveats regarding thresholds for diagnosing obesity and assessing health risks (Gurunathan & Myles, 2016). Ideally, practical BMI measurements used to define, diagnose, and assign treatment strategies of obesity have created an “obesity paradox” in medical settings.
BMI measurements calculate the ratio of a person’s weight and height (BMI=kg/m²). Previously, research has shown that BMI calculations offer good estimates and correctness of obesity, as well as providing important health outcomes, such as cancer, diabetes, heart diseases, and mortality (Gurunathan & Myles, 2016). However, Purnell (2018) posits that people can measure their fat mass using several imaging modalities, such as MRI, CT, and DEXA, despite the systems showing impracticability and being cost-prohibitive for normal medical use. Also, Purnell (2018) argues that clinics and fitness centers use underwater weighing, bioimpedance analyses, and air displacement methods to estimate the proportion of fat mass and percentage of body fat. However, a considerable number of researchers detest their use as other conditions that accompany obesity, such as chronic kidney disease or congestive heart failure, which alter the body’s fluid status may provide abnormal results (Purnell, 2018).
BMI calculations correlate well with the percentage of fat in the body, but previous scientific studies have shown that the relationship is self-reliantly influenced by a person’s sex, race, and age (Purnell, 2018). Also, Gurunathan and Myles (2016) assert that defining and describing obesity through BMI results leads to inaccurate assessments of adiposity as the measurements fail to distinguish lean muscles from fat mass. As shown by Figure 1.0, BMI fails to consider differences in the distribution of age or fat-related decrease in muscle mass. Purnell (2018) posits that statistics derived by BMI calculations to define and diagnose adults suggest that an “obesogenic” environment disproportionally affects populations portions with higher genetic potential for weight gain. To signify the “obesity paradox,” of using BMI to define and diagnose obesity, Purnell (2018) argues that the curves in Figure 1.0 look “abnormal” as it means only 30 percent of people living in the U.S. have a normal healthy weight.