Body fat percentage beats BMI in predicting obesity-related health risks, study says

In a recent study published in the Journal of Clinical Endocrinology & Metabolism, researchers assessed body fat percentage (%BF) thresholds to define overweight and obesity by examining their correlation with metabolic syndrome (MetSyn) in a substantial sample of adults in the United States.

The findings show that %BF thresholds are more precise than body mass index (BMI) for forecasting obesity-related health conditions. They advocate for the adoption of direct adiposity measurements in clinical settings and propose that overweight may be identified at 25% BF for men and 36% BF for women. In comparison, obesity can be marked at 30% BF for males and 42% BF for females.

Study: Defining Overweight and Obesity by Percent Body Fat instead of Body Mass Index. Image Credit: Fatseyeva / ShutterstockStudy: Defining Overweight and Obesity by Percent Body Fat instead of Body Mass Index. Image Credit: Fatseyeva / Shutterstock

Background

Typically, health standards use BMI to define obesity, overweight, and healthy weight thresholds. However, BMI is recognized as an imprecise measure of actual adiposity or %BF.

Modern technologies have improved the estimation of %BF, but outcome-based %BF thresholds are necessary so that these measurements can be used effectively to guide patient health.

Previous attempts to correlate %BF with health risks using BMI have been problematic due to imprecise relationships and the effect of factors like sex, age, fitness habits, and nutrition.

Obesity-linked diseases are associated with excess adiposity, yet current weight recommendations often rely on generalized mortality statistics rather than direct associations with specific health outcomes.

Historical standards often offer arbitrary weight targets based on population percentiles and simple anthropometrics due to the lack of practical alternatives.

Now, more accurate %BF estimation methods like multifrequency bioelectrical impedance (MF-BIA) are maturing and could play significant roles in preventive healthcare. Given the association between %BF and MetSyn, which affects a significant portion of adults, %BF metrics may offer a more precise tool for managing obesity-related diseases compared to BMI.

About the study

This study conducted correlational analyses using data from the National Health and Nutrition Examination Survey (NHANES) to evaluate %BF thresholds for defining overweight and obesity.

The sample included 16,918 individuals aged 18 to 85, with data collected between 1999 and 2018, excluding periods without dual-energy x-ray absorptiometry (DXA) measurements.

Data collected included demographics, laboratory measures including fasting glucose, triglycerides, high-density lipoprotein (HDL), cholesterol (HDL-C), and blood pressure; body measures (BMI, weight, height, and waist circumference); and whole-body DXA assessments.

Each participant's metabolic health was classified based on the presence of MetSyn, defined by meeting at least three of the five key markers: elevated waist circumference, low HDL-C, high fasting glucose, elevated blood pressure, and high triglycerides.

Descriptive statistics reported as mean and standard deviation or using the number of incidences were used to determine %BF thresholds for overweight and obesity based on associations with MetSyn.

Findings

The study analyzed data from 16,918 individuals (8,184 women and 8,734 men) with a mean age of approximately 42 years, representing various ethnic groups.

Among individuals classified as overweight (BMI >25 kg/m²) and obese (BMI ≥30 kg/m²), 5% and 35% had MetSyn, respectively. These rates were used to establish new %BF thresholds: 25% for overweight compared to 30% for obesity among males and 36% for overweight compared to 42% for obesity among females.

Using these %BF thresholds, 27.2% of women and 27.7% of men were classified as having a healthy weight, 33.5% of women and 34.0% of men were classified as overweight, and 39.4% of women and 38.3% of men were classified as obese.

The study highlighted that BMI has a poor predictive value for individuals due to significant variability in %BF at any given BMI.

Additionally, differences in how BMI correlates with %BF between men and women underscore the limitations of using BMI to assess adiposity and related health risks.

Conclusions

Recent advances in MF-BIA offer more reliable and affordable %BF assessments than traditional anthropometric methods.

However, the precision of these devices varies, and the increasing adoption of these technologies signifies a significant step towards broader clinical use and better epidemiological data.

 Although %BF and BMI showed similar Receiver Operating Characteristic (ROC) and Area Under the Curve  (AUC) statistics, highlighting differences across sex, the study suggests %BF might better capture the nuances of body composition related to metabolic diseases.

This study's %BF thresholds align with established guidelines and offer the potential for improved obesity-related disease management. While BMI remains a useful initial screening tool, it fails to accurately represent fat and lean body mass.

Technological improvements in body composition assessments, including more precise MF-BIA models and endorsements from medical societies, could enhance clinical applications and insurance coverage, ultimately benefiting patient care.

Limitations include variability in device accuracy and the need for further research on body composition and metabolic disease relationships.

Journal reference:
Priyanjana Pramanik

Written by

Priyanjana Pramanik

Priyanjana Pramanik is a writer based in Kolkata, India, with an academic background in Wildlife Biology and economics. She has experience in teaching, science writing, and mangrove ecology. Priyanjana holds Masters in Wildlife Biology and Conservation (National Centre of Biological Sciences, 2022) and Economics (Tufts University, 2018). In between master's degrees, she was a researcher in the field of public health policy, focusing on improving maternal and child health outcomes in South Asia. She is passionate about science communication and enabling biodiversity to thrive alongside people. The fieldwork for her second master's was in the mangrove forests of Eastern India, where she studied the complex relationships between humans, mangrove fauna, and seedling growth.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Pramanik, Priyanjana. (2024, May 20). Body fat percentage beats BMI in predicting obesity-related health risks, study says. News-Medical. Retrieved on June 15, 2024 from https://www.news-medical.net/news/20240519/Body-fat-percentage-beats-BMI-in-predicting-obesity-related-health-risks-study-says.aspx.

  • MLA

    Pramanik, Priyanjana. "Body fat percentage beats BMI in predicting obesity-related health risks, study says". News-Medical. 15 June 2024. <https://www.news-medical.net/news/20240519/Body-fat-percentage-beats-BMI-in-predicting-obesity-related-health-risks-study-says.aspx>.

  • Chicago

    Pramanik, Priyanjana. "Body fat percentage beats BMI in predicting obesity-related health risks, study says". News-Medical. https://www.news-medical.net/news/20240519/Body-fat-percentage-beats-BMI-in-predicting-obesity-related-health-risks-study-says.aspx. (accessed June 15, 2024).

  • Harvard

    Pramanik, Priyanjana. 2024. Body fat percentage beats BMI in predicting obesity-related health risks, study says. News-Medical, viewed 15 June 2024, https://www.news-medical.net/news/20240519/Body-fat-percentage-beats-BMI-in-predicting-obesity-related-health-risks-study-says.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Metabolic surgery proven most effective for sustained weight loss over GLP-1 drugs and lifestyle interventions