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Has the time come to bury BMI in favor of other screening measures?
In 1832, Belgian statistician Adolphe Quetelet introduced the concept of body mass index (BMI) – one’s weight (in kilograms) divided by the square of one’s height (in meters) as a measurement of ideal body weight. Approximately 140 years later, nutritional epidemiologist Ancel Keys proposed the use of BMI as a surrogate marker for evaluating body fat percentage within a population.
For the past 50 years, the scientific and medical communities have relied on BMI as a research and study tool to categorize patients’ weight (that is, severely underweight, underweight, normal weight, overweight, and obesity). The World Health Organization, National Institutes of Health, and U.S. Centers for Disease Control and Prevention use the following BMI weight classifications for adult patients:
- Underweight: BMI < 18.5
- Normal weight: BMI ≥ 18.5 to 24.9
- Overweight: BMI ≥ 25 to 29.9
- Obesity: BMI ≥ 30
Of note, BMI categories for children and adolescents (aged 2-19 years) are based on sex- and age-specific percentiles and will not be addressed in this article.
BMI appears to be a straightforward, easy, and cost-effective way to identify “healthy” weight and assess a patient’s risk for related conditions. For example, studies show that a BMI ≥ 35 kg/m2 correlates to higher prevalence of type 2 diabetes, hypertension, dyslipidemia, and decreased lifespan. At least 13 types of cancer have been linked to obesity, regardless of dietary or physical activity behaviors. While the health dangers associated with BMI ≥ 35 are substantial and difficult to dispute, concerns arise when BMI alone is used to determine healthy weight and disease risk in patients with a BMI of 25-35.
BMI limitations
There are troubling limitations to using BMI alone to assess a patient’s weight and health status. BMI only takes into account a patient’s height and weight, neither of which are sole determinants of health. Moreover, BMI measurements do not distinguish between fat mass and fat-free mass, each of which has very distinct effects on health. High fat mass is associated with an increased risk for disease and mortality, while higher lean body mass correlates with increased physical fitness and longevity. BMI also does not consider age, sex, race, ethnicity, or types of adipose tissue, all of which tremendously influence disease risk across all BMI categories.
Body composition and adipose tissue
Body composition and type of excess adipose tissue better correlate disease risk than does BMI. The World Health Organization defines obesity as having a body fat percentage > 25% for men and > 35% for women. Body composition can be measured by skin-fold thickness, bioelectrical impedance, dual-energy x-ray absorptiometry (DXA), CT, or MRI.
A cross-sectional study by Shah and colleagues) comparing BMI and DXA found that BMI underestimated obesity prevalence. In the study, BMI characterized 26% of participants as obese while DXA (a direct measurement of fat) characterized 64%. Further, 39% of patients categorized as nonobese based on BMI were found to be obese on DXA. Also, BMI misclassified 25% of men and 48% of women in the study. These findings and those of other studies suggest that BMI has a high specificity but low sensitivity for diagnosing obesity, questioning its reliability as a clinical screening tool.
Current guideline recommendations on pharmacologic and surgical treatment options for patients with overweight or obesity, including those of the American Association of Clinical Endocrinology and American College of Endocrinology (AACE/ACE) and the American College of Cardiology/American Heart Association and The Obesity Society (ACC/AHA/TOS), rely on BMI, diminishing their utilization. For example, a recent literature search by Li and associates found that Asian American patients with lower BMIs and BMIs of 25 or 27 are at increased risk for metabolic disease. On the basis of study findings, some organizations recommend considering pharmacotherapy at a lower BMI cutoff of ≥ 25.0 or ≥ 27.5 for Asian people to ensure early treatment intervention in this patient population because guidelines do not recommend pharmacologic treatment unless the BMI is 27 with weight-related complications or 30. Under the current guidelines, a patient of Asian descent has greater disease severity with potentially more complications by the time pharmacotherapy is initiated.
As previously noted, body composition, which requires the use of special equipment (skinfold calipers, DXA, CT, MRI, body impedance scale), best captures the ratio of fat mass to fat-free mass. DXA is frequently used in research studies looking at body composition because of its lower cost, faster time to obtain the study, and ability to measure bone density. MRI has been found to be as accurate as CT for assessing visceral adipose tissue (VAT), skeletal muscle mass, and organ mass, and does not expose patients to ionizing radiation like CT does. MRI clinical use, however, is limited because of its high cost, and it may be problematic for patients with claustrophobia or who are unable to remain immobile for an extended period.
Patients with a high VAT mass, compared with subcutaneous adipose tissue (SAT), are at increased risk for metabolic syndrome, nonalcoholic fatty liver disease, and cardiovascular disease regardless of BMI, underscoring the clinical usefulness of measuring visceral adiposity over BMI.
One of the barriers to implementing VAT assessment in clinical practice is the cost of imaging studies. Fortunately, data suggest that waist circumference and/or waist-to-hip ratio measurements can be a valuable surrogate for VAT measurement. A waist circumference greater than 35 inches (88 cm) or a waist-to-hip ratio greater than 0.8 for women, and greater than 40 inches (102 cm) or a waist-to-hip ratio greater than 0.95 for men, increases metabolic disease risk. Obtaining these measurements requires a tape measure and a few extra minutes and offers more potent data than BMI alone. For example, a large cardiometabolic study found that within each BMI category, increasing gender-specific waist circumferences were associated with significantly higher VAT, liver fat, and a more harmful cardiometabolic risk profile. Men and women with a lower or normal BMI and a high waist circumference are at greatest relative health risk, compared with those with low waist circumference values. Yet, using the BMI alone in these patients would not raise any clinical concern, which is a missed opportunity for cardiometabolic risk reduction.
Biomarkers
Specific biomarkers are closely related to obesity. Leptin and resistin protein levels increase with adipose mass, while adiponectin decreases, probably contributing to insulin resistance. The higher levels of tumor necrosis factor–alpha and interleukin-6 from obesity contribute to chronic inflammation. The combined effect of chronic inflammation and insulin resistance allows greater bioavailability of insulinlike growth factor-1 (IGF-1), which has a role in initiating type 2 diabetes, cardiovascular disease, and cancer. Ideally, measuring these biomarkers could provide more advantageous information than BMI. Unfortunately, for now, the lack of standardized assays and imperfect knowledge of exactly how these biomarkers elicit disease prevents clinical use.
Obesity is a common, highly complex, chronic, and relapsing disease. Thankfully, a number of effective treatments and interventions are available. Although an accurate diagnosis of obesity is essential, underdiagnosed cases and missed opportunities for metabolic disease risk reduction persist. Overdiagnosing obesity, however, has the potential to incur unnecessary health care costs and result in weight bias and stigma.
While BMI is a quick and inexpensive means to assess obesity, by itself it lacks the necessary components for an accurate diagnosis. Particularly for individuals with a normal BMI or less severe overweight/obesity (BMI 27-34.9), other factors must be accounted for, including age, gender, and race. At a minimum, waist circumference should be measured to best risk-stratify and determine treatment intensity. Body composition analysis with BMI calculation refines the diagnosis of obesity.
Finally, clinicians may find best practices by using BMI delta change models. As with so many other clinical measurements, the trajectory tells the most astute story. For example, a patient whose BMI decreased from 45 to 35 may warrant less intensive treatment than a patient whose BMI increased from 26 to 31. Any change in BMI warrants clinical attention. A rapidly or consistently increasing BMI, even within normal range, should prompt clinicians to assess other factors related to obesity and metabolic disease risk (for example, lifestyle factors, waist circumference, blood pressure, cholesterol, diabetes screening) and initiate a conversation about weight management. Similarly, a consistently or rapidly decreasing BMI – even in elevated ranges and particularly with unintentional weight loss – should prompt evaluation.
Although BMI continues to be useful in clinical practice, epidemiology, and research, it should be used in combination with other clinical factors to provide the utmost quality of care.
Dr. Bartfield is assistant professor, obesity medicine specialist, Wake Forest Baptists Medical Center/Atrium Health Weight Management Center, Greensboro, N.C. She has disclosed no relevant financial relationships.
A version of this article appeared on Medscape.com.
In 1832, Belgian statistician Adolphe Quetelet introduced the concept of body mass index (BMI) – one’s weight (in kilograms) divided by the square of one’s height (in meters) as a measurement of ideal body weight. Approximately 140 years later, nutritional epidemiologist Ancel Keys proposed the use of BMI as a surrogate marker for evaluating body fat percentage within a population.
For the past 50 years, the scientific and medical communities have relied on BMI as a research and study tool to categorize patients’ weight (that is, severely underweight, underweight, normal weight, overweight, and obesity). The World Health Organization, National Institutes of Health, and U.S. Centers for Disease Control and Prevention use the following BMI weight classifications for adult patients:
- Underweight: BMI < 18.5
- Normal weight: BMI ≥ 18.5 to 24.9
- Overweight: BMI ≥ 25 to 29.9
- Obesity: BMI ≥ 30
Of note, BMI categories for children and adolescents (aged 2-19 years) are based on sex- and age-specific percentiles and will not be addressed in this article.
BMI appears to be a straightforward, easy, and cost-effective way to identify “healthy” weight and assess a patient’s risk for related conditions. For example, studies show that a BMI ≥ 35 kg/m2 correlates to higher prevalence of type 2 diabetes, hypertension, dyslipidemia, and decreased lifespan. At least 13 types of cancer have been linked to obesity, regardless of dietary or physical activity behaviors. While the health dangers associated with BMI ≥ 35 are substantial and difficult to dispute, concerns arise when BMI alone is used to determine healthy weight and disease risk in patients with a BMI of 25-35.
BMI limitations
There are troubling limitations to using BMI alone to assess a patient’s weight and health status. BMI only takes into account a patient’s height and weight, neither of which are sole determinants of health. Moreover, BMI measurements do not distinguish between fat mass and fat-free mass, each of which has very distinct effects on health. High fat mass is associated with an increased risk for disease and mortality, while higher lean body mass correlates with increased physical fitness and longevity. BMI also does not consider age, sex, race, ethnicity, or types of adipose tissue, all of which tremendously influence disease risk across all BMI categories.
Body composition and adipose tissue
Body composition and type of excess adipose tissue better correlate disease risk than does BMI. The World Health Organization defines obesity as having a body fat percentage > 25% for men and > 35% for women. Body composition can be measured by skin-fold thickness, bioelectrical impedance, dual-energy x-ray absorptiometry (DXA), CT, or MRI.
A cross-sectional study by Shah and colleagues) comparing BMI and DXA found that BMI underestimated obesity prevalence. In the study, BMI characterized 26% of participants as obese while DXA (a direct measurement of fat) characterized 64%. Further, 39% of patients categorized as nonobese based on BMI were found to be obese on DXA. Also, BMI misclassified 25% of men and 48% of women in the study. These findings and those of other studies suggest that BMI has a high specificity but low sensitivity for diagnosing obesity, questioning its reliability as a clinical screening tool.
Current guideline recommendations on pharmacologic and surgical treatment options for patients with overweight or obesity, including those of the American Association of Clinical Endocrinology and American College of Endocrinology (AACE/ACE) and the American College of Cardiology/American Heart Association and The Obesity Society (ACC/AHA/TOS), rely on BMI, diminishing their utilization. For example, a recent literature search by Li and associates found that Asian American patients with lower BMIs and BMIs of 25 or 27 are at increased risk for metabolic disease. On the basis of study findings, some organizations recommend considering pharmacotherapy at a lower BMI cutoff of ≥ 25.0 or ≥ 27.5 for Asian people to ensure early treatment intervention in this patient population because guidelines do not recommend pharmacologic treatment unless the BMI is 27 with weight-related complications or 30. Under the current guidelines, a patient of Asian descent has greater disease severity with potentially more complications by the time pharmacotherapy is initiated.
As previously noted, body composition, which requires the use of special equipment (skinfold calipers, DXA, CT, MRI, body impedance scale), best captures the ratio of fat mass to fat-free mass. DXA is frequently used in research studies looking at body composition because of its lower cost, faster time to obtain the study, and ability to measure bone density. MRI has been found to be as accurate as CT for assessing visceral adipose tissue (VAT), skeletal muscle mass, and organ mass, and does not expose patients to ionizing radiation like CT does. MRI clinical use, however, is limited because of its high cost, and it may be problematic for patients with claustrophobia or who are unable to remain immobile for an extended period.
Patients with a high VAT mass, compared with subcutaneous adipose tissue (SAT), are at increased risk for metabolic syndrome, nonalcoholic fatty liver disease, and cardiovascular disease regardless of BMI, underscoring the clinical usefulness of measuring visceral adiposity over BMI.
One of the barriers to implementing VAT assessment in clinical practice is the cost of imaging studies. Fortunately, data suggest that waist circumference and/or waist-to-hip ratio measurements can be a valuable surrogate for VAT measurement. A waist circumference greater than 35 inches (88 cm) or a waist-to-hip ratio greater than 0.8 for women, and greater than 40 inches (102 cm) or a waist-to-hip ratio greater than 0.95 for men, increases metabolic disease risk. Obtaining these measurements requires a tape measure and a few extra minutes and offers more potent data than BMI alone. For example, a large cardiometabolic study found that within each BMI category, increasing gender-specific waist circumferences were associated with significantly higher VAT, liver fat, and a more harmful cardiometabolic risk profile. Men and women with a lower or normal BMI and a high waist circumference are at greatest relative health risk, compared with those with low waist circumference values. Yet, using the BMI alone in these patients would not raise any clinical concern, which is a missed opportunity for cardiometabolic risk reduction.
Biomarkers
Specific biomarkers are closely related to obesity. Leptin and resistin protein levels increase with adipose mass, while adiponectin decreases, probably contributing to insulin resistance. The higher levels of tumor necrosis factor–alpha and interleukin-6 from obesity contribute to chronic inflammation. The combined effect of chronic inflammation and insulin resistance allows greater bioavailability of insulinlike growth factor-1 (IGF-1), which has a role in initiating type 2 diabetes, cardiovascular disease, and cancer. Ideally, measuring these biomarkers could provide more advantageous information than BMI. Unfortunately, for now, the lack of standardized assays and imperfect knowledge of exactly how these biomarkers elicit disease prevents clinical use.
Obesity is a common, highly complex, chronic, and relapsing disease. Thankfully, a number of effective treatments and interventions are available. Although an accurate diagnosis of obesity is essential, underdiagnosed cases and missed opportunities for metabolic disease risk reduction persist. Overdiagnosing obesity, however, has the potential to incur unnecessary health care costs and result in weight bias and stigma.
While BMI is a quick and inexpensive means to assess obesity, by itself it lacks the necessary components for an accurate diagnosis. Particularly for individuals with a normal BMI or less severe overweight/obesity (BMI 27-34.9), other factors must be accounted for, including age, gender, and race. At a minimum, waist circumference should be measured to best risk-stratify and determine treatment intensity. Body composition analysis with BMI calculation refines the diagnosis of obesity.
Finally, clinicians may find best practices by using BMI delta change models. As with so many other clinical measurements, the trajectory tells the most astute story. For example, a patient whose BMI decreased from 45 to 35 may warrant less intensive treatment than a patient whose BMI increased from 26 to 31. Any change in BMI warrants clinical attention. A rapidly or consistently increasing BMI, even within normal range, should prompt clinicians to assess other factors related to obesity and metabolic disease risk (for example, lifestyle factors, waist circumference, blood pressure, cholesterol, diabetes screening) and initiate a conversation about weight management. Similarly, a consistently or rapidly decreasing BMI – even in elevated ranges and particularly with unintentional weight loss – should prompt evaluation.
Although BMI continues to be useful in clinical practice, epidemiology, and research, it should be used in combination with other clinical factors to provide the utmost quality of care.
Dr. Bartfield is assistant professor, obesity medicine specialist, Wake Forest Baptists Medical Center/Atrium Health Weight Management Center, Greensboro, N.C. She has disclosed no relevant financial relationships.
A version of this article appeared on Medscape.com.
In 1832, Belgian statistician Adolphe Quetelet introduced the concept of body mass index (BMI) – one’s weight (in kilograms) divided by the square of one’s height (in meters) as a measurement of ideal body weight. Approximately 140 years later, nutritional epidemiologist Ancel Keys proposed the use of BMI as a surrogate marker for evaluating body fat percentage within a population.
For the past 50 years, the scientific and medical communities have relied on BMI as a research and study tool to categorize patients’ weight (that is, severely underweight, underweight, normal weight, overweight, and obesity). The World Health Organization, National Institutes of Health, and U.S. Centers for Disease Control and Prevention use the following BMI weight classifications for adult patients:
- Underweight: BMI < 18.5
- Normal weight: BMI ≥ 18.5 to 24.9
- Overweight: BMI ≥ 25 to 29.9
- Obesity: BMI ≥ 30
Of note, BMI categories for children and adolescents (aged 2-19 years) are based on sex- and age-specific percentiles and will not be addressed in this article.
BMI appears to be a straightforward, easy, and cost-effective way to identify “healthy” weight and assess a patient’s risk for related conditions. For example, studies show that a BMI ≥ 35 kg/m2 correlates to higher prevalence of type 2 diabetes, hypertension, dyslipidemia, and decreased lifespan. At least 13 types of cancer have been linked to obesity, regardless of dietary or physical activity behaviors. While the health dangers associated with BMI ≥ 35 are substantial and difficult to dispute, concerns arise when BMI alone is used to determine healthy weight and disease risk in patients with a BMI of 25-35.
BMI limitations
There are troubling limitations to using BMI alone to assess a patient’s weight and health status. BMI only takes into account a patient’s height and weight, neither of which are sole determinants of health. Moreover, BMI measurements do not distinguish between fat mass and fat-free mass, each of which has very distinct effects on health. High fat mass is associated with an increased risk for disease and mortality, while higher lean body mass correlates with increased physical fitness and longevity. BMI also does not consider age, sex, race, ethnicity, or types of adipose tissue, all of which tremendously influence disease risk across all BMI categories.
Body composition and adipose tissue
Body composition and type of excess adipose tissue better correlate disease risk than does BMI. The World Health Organization defines obesity as having a body fat percentage > 25% for men and > 35% for women. Body composition can be measured by skin-fold thickness, bioelectrical impedance, dual-energy x-ray absorptiometry (DXA), CT, or MRI.
A cross-sectional study by Shah and colleagues) comparing BMI and DXA found that BMI underestimated obesity prevalence. In the study, BMI characterized 26% of participants as obese while DXA (a direct measurement of fat) characterized 64%. Further, 39% of patients categorized as nonobese based on BMI were found to be obese on DXA. Also, BMI misclassified 25% of men and 48% of women in the study. These findings and those of other studies suggest that BMI has a high specificity but low sensitivity for diagnosing obesity, questioning its reliability as a clinical screening tool.
Current guideline recommendations on pharmacologic and surgical treatment options for patients with overweight or obesity, including those of the American Association of Clinical Endocrinology and American College of Endocrinology (AACE/ACE) and the American College of Cardiology/American Heart Association and The Obesity Society (ACC/AHA/TOS), rely on BMI, diminishing their utilization. For example, a recent literature search by Li and associates found that Asian American patients with lower BMIs and BMIs of 25 or 27 are at increased risk for metabolic disease. On the basis of study findings, some organizations recommend considering pharmacotherapy at a lower BMI cutoff of ≥ 25.0 or ≥ 27.5 for Asian people to ensure early treatment intervention in this patient population because guidelines do not recommend pharmacologic treatment unless the BMI is 27 with weight-related complications or 30. Under the current guidelines, a patient of Asian descent has greater disease severity with potentially more complications by the time pharmacotherapy is initiated.
As previously noted, body composition, which requires the use of special equipment (skinfold calipers, DXA, CT, MRI, body impedance scale), best captures the ratio of fat mass to fat-free mass. DXA is frequently used in research studies looking at body composition because of its lower cost, faster time to obtain the study, and ability to measure bone density. MRI has been found to be as accurate as CT for assessing visceral adipose tissue (VAT), skeletal muscle mass, and organ mass, and does not expose patients to ionizing radiation like CT does. MRI clinical use, however, is limited because of its high cost, and it may be problematic for patients with claustrophobia or who are unable to remain immobile for an extended period.
Patients with a high VAT mass, compared with subcutaneous adipose tissue (SAT), are at increased risk for metabolic syndrome, nonalcoholic fatty liver disease, and cardiovascular disease regardless of BMI, underscoring the clinical usefulness of measuring visceral adiposity over BMI.
One of the barriers to implementing VAT assessment in clinical practice is the cost of imaging studies. Fortunately, data suggest that waist circumference and/or waist-to-hip ratio measurements can be a valuable surrogate for VAT measurement. A waist circumference greater than 35 inches (88 cm) or a waist-to-hip ratio greater than 0.8 for women, and greater than 40 inches (102 cm) or a waist-to-hip ratio greater than 0.95 for men, increases metabolic disease risk. Obtaining these measurements requires a tape measure and a few extra minutes and offers more potent data than BMI alone. For example, a large cardiometabolic study found that within each BMI category, increasing gender-specific waist circumferences were associated with significantly higher VAT, liver fat, and a more harmful cardiometabolic risk profile. Men and women with a lower or normal BMI and a high waist circumference are at greatest relative health risk, compared with those with low waist circumference values. Yet, using the BMI alone in these patients would not raise any clinical concern, which is a missed opportunity for cardiometabolic risk reduction.
Biomarkers
Specific biomarkers are closely related to obesity. Leptin and resistin protein levels increase with adipose mass, while adiponectin decreases, probably contributing to insulin resistance. The higher levels of tumor necrosis factor–alpha and interleukin-6 from obesity contribute to chronic inflammation. The combined effect of chronic inflammation and insulin resistance allows greater bioavailability of insulinlike growth factor-1 (IGF-1), which has a role in initiating type 2 diabetes, cardiovascular disease, and cancer. Ideally, measuring these biomarkers could provide more advantageous information than BMI. Unfortunately, for now, the lack of standardized assays and imperfect knowledge of exactly how these biomarkers elicit disease prevents clinical use.
Obesity is a common, highly complex, chronic, and relapsing disease. Thankfully, a number of effective treatments and interventions are available. Although an accurate diagnosis of obesity is essential, underdiagnosed cases and missed opportunities for metabolic disease risk reduction persist. Overdiagnosing obesity, however, has the potential to incur unnecessary health care costs and result in weight bias and stigma.
While BMI is a quick and inexpensive means to assess obesity, by itself it lacks the necessary components for an accurate diagnosis. Particularly for individuals with a normal BMI or less severe overweight/obesity (BMI 27-34.9), other factors must be accounted for, including age, gender, and race. At a minimum, waist circumference should be measured to best risk-stratify and determine treatment intensity. Body composition analysis with BMI calculation refines the diagnosis of obesity.
Finally, clinicians may find best practices by using BMI delta change models. As with so many other clinical measurements, the trajectory tells the most astute story. For example, a patient whose BMI decreased from 45 to 35 may warrant less intensive treatment than a patient whose BMI increased from 26 to 31. Any change in BMI warrants clinical attention. A rapidly or consistently increasing BMI, even within normal range, should prompt clinicians to assess other factors related to obesity and metabolic disease risk (for example, lifestyle factors, waist circumference, blood pressure, cholesterol, diabetes screening) and initiate a conversation about weight management. Similarly, a consistently or rapidly decreasing BMI – even in elevated ranges and particularly with unintentional weight loss – should prompt evaluation.
Although BMI continues to be useful in clinical practice, epidemiology, and research, it should be used in combination with other clinical factors to provide the utmost quality of care.
Dr. Bartfield is assistant professor, obesity medicine specialist, Wake Forest Baptists Medical Center/Atrium Health Weight Management Center, Greensboro, N.C. She has disclosed no relevant financial relationships.
A version of this article appeared on Medscape.com.