Introduction
Obesity has become a critical public health concern in the United States and globally [1]. A significant portion of the population is categorized as overweight or obese, raising concerns about associated health risks [2,3]. To address this, various methods are employed to assess body composition and obesity levels, including body mass index (BMI), waist-to-hip ratio, body fat percentage, and waist circumference [4]. Among these, body mass index (BMI) stands out as a widely used screening tool that compares height and weight to provide an initial assessment of an individual’s weight status [5].
BMI, derived from a simple calculation using height and weight, serves as a valuable indicator for potential health risks. It is routinely used to screen for the likelihood of developing chronic conditions such as diabetes, hypertension, depression, and certain cancers [4–8]. The resulting BMI value falls within defined ranges, categorizing individuals and helping healthcare professionals and researchers to communicate potential health risks effectively [9]. Ongoing research continues to explore the correlation between BMI and chronic illnesses, as well as the predictive capabilities of other measurements like waist circumference [10]. Furthermore, with childhood obesity on the rise, the role of childhood BMI as a predictor of later-life health outcomes is gaining increasing attention [11].
This review provides a concise overview of BMI as a screening tool that compares height and weight for chronic disease and mortality risk assessment. It also touches upon the significance of childhood BMI in predicting future health. While BMI offers valuable insights, it is essential to acknowledge its limitations, which will also be discussed.
Review of BMI as a Health Screening Tool
Literature Review Methodology
To gather relevant information for this review, a comprehensive search of scientific journal articles published between 1994 and 2019 was conducted using the PubMed database and references from relevant articles. The primary search term was ‘body mass index’. This search yielded 30 articles investigating the relationship between BMI and metabolic conditions such as diabetes, hypertension, and mortality associated with disease. The search was broadened to include articles discussing BMI in relation to waist circumference, waist-hip ratio, BMI in youth as a health indicator, and the limitations of BMI as a tool for health evaluation. The evidence from these studies allows for extrapolating the connections between BMI and chronic disease variables to broader populations.
BMI and Diabetes Risk
Several studies have investigated the link between BMI and the risk of developing type 2 diabetes. A study in the U.S. by Chan et al. [12] involving over 50,000 male healthcare professionals aged 40-75, found that individuals with a BMI exceeding 35 kg/m² had a significantly higher risk of developing type 2 diabetes compared to those with a BMI below 23 kg/m². While this study reinforces the association between higher BMI and increased diabetes risk, it’s important to consider the study’s timeframe (data collected in 1994).
More recent research has explored the universality of these findings across different populations. A Turkish study involving over 26,000 individuals across various regions of Turkey examined diabetes risk factors [13]. The study considered factors like family history, age, education, waist circumference, BMI, hypertension, smoking, and meal frequency. Across all demographics, a one standard deviation increase in waist circumference was associated with a 1.16-fold increased likelihood of a new diabetes diagnosis. In men, this risk was even higher at 1.28-fold.
Satman et al.’s research also supports BMI as a significant indicator of diabetes risk [13]. Their findings indicated that a one standard deviation increase in BMI (5.9 kg/m²) led to a 1.16 increased probability of type 2 diabetes for men and 1.09 for women.
Caption: Illustration demonstrating how BMI is calculated using height and weight, and the BMI ranges for different weight categories.
Furthermore, a multi-ethnic, longitudinal study in Canada involving nearly 60,000 non-diabetic adults revealed that the risk of developing diabetes increases at lower BMI values for certain ethnic groups compared to White individuals [14]. South Asian individuals showed increased risk at a BMI of 24 kg/m², Black individuals at 26 kg/m², and Chinese individuals at 25 kg/m², compared to White individuals at 30 kg/m². These findings highlight the importance of considering ethnicity when using BMI as a screening tool that compares height and weight for diabetes risk and can inform personalized care plans and public health strategies.
While BMI is a useful screening tool for diabetes risk, some studies suggest that other measures might be more effective. A meta-analysis by Lee et al. [15] involving 80,000 participants across 10 studies found that BMI was the least effective predictor for cardiovascular risk factors including diabetes, hypertension, and dyslipidemia compared to waist circumference, waist-to-hip ratio, and waist-to-height ratio. The waist-to-height ratio was found to be particularly superior in predicting these risks, potentially due to its ability to account for central adiposity, a factor BMI does not directly assess.
BMI and Hypertension Risk
Similar research has examined the association between BMI and hypertension. Hu et al. [16] analyzed data from over 17,000 Finnish individuals collected between 1982 and 1992, assessing height, weight, and heart rate, among other factors. The study used hazard ratios and follow-up examinations to assess the predictive power of BMI for hypertension. Results showed a clear trend: higher BMI categories were associated with increased hypertension risk (BMI <25= 1.00, 25-29.9= 1.18, and >30= 1.66). The study also noted that physical activity significantly reduced hypertension risk, even in individuals with higher BMIs.
Gelber et al. [17] followed over 13,000 non-hypertensive subjects for 14 years to investigate the link between baseline BMI and the development of hypertension. Their findings indicated that even individuals with BMIs in the higher end of the “normal” range had an elevated risk of developing hypertension compared to those in the lowest BMI quintile (<22.5 kg/m²). A positive correlation was observed between BMI at baseline and hypertension development after 14 years. Participants with a BMI greater than 26.4 kg/m² had a 1.85 times greater risk of becoming hypertensive. These studies collectively demonstrate the value of BMI as a screening tool that compares height and weight for identifying individuals at higher risk of hypertension and understanding cardiovascular disease etiologies.
BMI and Hypercholesterolemia
Obesity is frequently linked to dyslipidemia, characterized by elevated serum triglycerides and LDL cholesterol, which are associated with increased body weight. Gostynski et al. [18] analyzed data from over 48,000 subjects aged 25-64 years to examine the relationship between BMI and hypercholesterolemia (≥6.5 mmol/l). They found a statistically significant positive correlation between BMI and hypercholesterolemia in the 25-50 year age group, with the strongest effect in the 25-39 year range.
The Bogalusa Heart Study and Tershakovec et al. [19] investigated the link between childhood obesity in girls and persistent hypercholesterolemia. Over 3,000 children aged 5-14 years were assessed for lipoprotein levels and evaluated against BMI and cholesterol serum normal values between 1973 and 1991. The study found that girls with high cholesterol were more likely to experience a greater increase in BMI than girls with normal cholesterol levels as they grew older. This suggests a correlation between high cholesterol and BMI development, highlighting the importance of early public health interventions to promote healthy development and reduce the risk of metabolic and cardiovascular diseases.
BMI and Mortality Risk
A large-scale study in the U.K. by Bhaskaran et al. [20] utilized a national mortality database of over 3.6 million people to assess the association between BMI and all-cause and cause-specific mortality. The researchers used hazard ratios and sensitivity analyses, adjusting for factors like age, sex, smoking, and diabetes. Focusing on the cohort of nearly 2 million never-smokers to eliminate smoking as a variable, the data revealed a J-shaped curve relationship between BMI and mortality. Mortality risk was elevated for individuals with a BMI above 25 kg/m². Notably, the lowest mortality risk was observed at a BMI of 25 kg/m². Obesity was associated with a significant reduction in life expectancy: 4.2 years for a 40-year-old male never-smoker and 3.5 years for a 40-year-old female never-smoker, compared to individuals with a healthy weight [20]. This underscores the importance of maintaining a healthy BMI for longevity.
BMI and Waist Circumference
Both BMI and waist circumference are used as measurements for assessing obesity. Chinedu et al. [21] directly compared these two measures in nearly 500 Nigerian participants aged 19-75 years. Their findings showed a statistically significant positive correlation between waist circumference and BMI (r=0.75). Similarly, Gierach et al. [22] found a positive correlation (r=0.78) between waist circumference and BMI in a study of over 800 individuals aged 32-80 years diagnosed with metabolic syndrome. These studies confirm that BMI, as a screening tool that compares height and weight, correlates with waist circumference, another important indicator of body fat distribution and health risk.
Romero-Corral et al. [23] conducted a BMI comparison study involving over 13,000 Americans, comparing BMI to body fat percentage (BF%) measured by bioelectrical impedance analysis (BIA). The study aimed to evaluate BMI’s effectiveness in diagnosing obesity against the World Health Organization’s BF% standards (>25% in men and >35% in women). The results indicated that BMI has high specificity but low sensitivity for detecting obesity, meaning it is good at correctly identifying non-obese individuals but often misses cases of obesity. Specifically, BMI was more closely associated with lean-body mass than BF% in men and less reliable for diagnosing obesity in older adults. In contrast, Flegal et al. [24] found that waist circumference was a better predictor of body fat percentage than BMI in men. The optimal measure for assessing obesity remains debated, but both BMI and waist circumference have their strengths and limitations.
BMI and Waist-to-Hip Ratio
While waist circumference is a useful prognostic tool for obesity, the waist-to-hip ratio is another relevant measurement. Bener et al. [25] compared four obesity measures: waist circumference, BMI, waist-to-height ratio, and waist-to-hip ratio. They concluded that waist circumference was the best measure of obesity in both men and women. For men, the waist-to-hip ratio was the second-best indicator, showing a larger area under the curve (AUC) than BMI.
Dalton et al. [26] studied Australian participants, adjusting for age, and found no significant differences between BMI, waist circumference, and waist-to-hip ratio in predicting obesity and associated chronic diseases (Table 1).
Table 1. Studies Evaluating the Correlation Between BMI and Other Body Composition Evaluation Tools
| Study, year | Population | Study Duration | Significant Findings |
|---|---|---|---|
| Chinedu et al. [21] 2013 | 489 Nigerian participants, 18-75 years old | April 2012 to May 2012 | Positive and statistically significant correlation (r=0.75) between BMI and waist circumference (WC) |
| Gierach et al. [22] 2014 | 839 participants with metabolic syndrome, 32-80 years | 24-month period, cross-sectional study | WC and BMI are correlated; significant- positive relationship (r=0.78) |
| Romero-Corral et al. [23] 2008 | 13,601 participants 20-79 years old | NHANES survey data integrated into cross-sectional study | BMI ≥ 30 kg/m2 has higher specificity but lower sensitivity for VF% obesity. |
| Flegal et al. [24] 2008 | 12,901 participants ≥ 18 years | Data collected 1999-2004; NHANES database | WC and BMI were better correlated with one another than BF% |
| Bener et al. [25] 2013 | 1,552 participants ≥ 20 years | April 2011- December 2012 integrated cross-sectional study | Best predictor of metabolic syndrome is WC, 2nd best in males is WHR |
| Dalton et al. [26] 2003 | 11,247 participants ≥ 25 years | A cross-sectional study with data gathered in 2000 | No fundamental difference between BMI, WHR, WC for evaluating obesity and chronic disease. |
BMI and Muscle Mass Considerations
While BMI is a useful screening tool for general population health risk assessment, it has limitations. It does not differentiate between muscle mass and fat mass [38]. Individuals with high muscle mass, such as athletes, may be misclassified as overweight or obese based solely on BMI. Kyle et al. [27] found that BMI alone is insufficient to determine the contribution of fat-free mass to body weight. Their study showed that while 71% of individuals with normal BMI also had normal fat-free mass index (FFMI), some individuals classified as obese by BMI had normal FFMI.
Furthermore, muscle mass naturally decreases with age, while fat mass increases. Elderly individuals may have normal or even low BMI scores despite having an unhealthy fat-to-muscle ratio and increased risk of secondary illnesses [28]. Body composition also varies based on sex assigned at birth. BMI, calculated simply from height and weight, does not account for these variations or other factors like pregnancy, breastfeeding, or certain medical conditions [29]. A European study comparing BMI to DXA and densitometry measurements found that BMI inaccurately classified 7% of women and 8% of men as obese based on body fat percentage guidelines [30]. These limitations highlight the need to use BMI cautiously and consider other factors in health assessments.
Childhood BMI as a Health Predictor
Research indicates that childhood BMI is a significant predictor of adult health outcomes. Twig et al. [31] studied teenagers in Israel and found a positive association between childhood BMI and cardiovascular-related death in adulthood. The association was present even within the 50th to 74th BMI percentile range and stronger above the 95th percentile.
Numerous studies support the link between high childhood BMI and later-life disease risk. One study found a correlation between increased childhood BMI and a higher incidence of type 2 diabetes in adulthood [32]. Childhood BMI has also been linked to an increased risk of endometrial cancer in women [33]. However, Aarestrup et al. [34] found no association between childhood BMI and prostate cancer risk in men after adjusting for height, suggesting sex-specific differences in BMI’s predictive power.
Beyond physical health, childhood BMI is also associated with mental health and sleep patterns. Studies have explored the link between high childhood BMI and suicidal behavior, potentially mediated by increased rates of depression in individuals with higher BMI [35]. Pileggi et al. [36] found that shorter sleep duration in middle-school children was associated with higher BMI, suggesting a bidirectional relationship between sleep and weight.
Socioeconomic factors also play a role in childhood BMI. Bai et al. [37] found that socioeconomic status, indicated by eligibility for free or reduced lunch, was a significant predictor of high BMI in over 2.5 million children and adolescents in Texas. This highlights the influence of social determinants of health on childhood obesity and BMI.
Limitations of BMI as a Health Assessment Tool
While BMI is a widely accessible and inexpensive screening tool that compares height and weight, it is crucial to acknowledge its limitations. As mentioned earlier, BMI does not directly measure body fat percentage and cannot distinguish between muscle mass and fat mass [38]. This can lead to misclassification, particularly for athletes and individuals with varying body compositions due to age, sex, and ethnicity [38]. Combining BMI with waist circumference measurements can improve the accuracy and validity of health status assessments [39].
Caption: Diagram illustrating the formula for calculating Body Mass Index (BMI) using weight in kilograms and height in meters squared.
Conclusion
BMI is a valuable screening tool that compares height and weight and correlates with various health outcomes and mortality risks across the lifespan, starting from childhood. However, relying solely on BMI to categorize obesity and predict chronic disease risk is not optimal. Other measures like waist circumference and waist-to-hip ratio may provide more nuanced assessments, especially when considering central adiposity and body fat distribution. If BMI is used as a screening tool, it should be interpreted cautiously, considering individual variability in sex, age, muscle mass, and ethnicity. Integrating BMI with other measurements and clinical assessments can lead to more personalized and effective health management and nutrition counseling.
Footnotes
The authors have declared that no competing interests exist.
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