Is BMI the optimal adiposity marker to assess mortality risk?

In a recent study published in the JAMA Network Open Journal, researchers evaluated adiposity markers such as waist-to-hip ratio (WHR), fat mass index (FMI), and body mass index (BMI), to determine the marker with the most consistent and strongest association with cause-specific and all-cause mortality.

Study: Surrogate Adiposity Markers and Mortality. Image Credit:VGstockstudio/Shutterstock.comStudy: Surrogate Adiposity Markers and Mortality. Image Credit:VGstockstudio/


The global prevalence of obesity and overweight has steadily increased in the last few decades, and many studies have shown that obesity is associated with a higher risk of disease and mortality.

Body mass index, which is body weight divided by the square of the body height, is currently used as the basis for recommendations to treat obesity.

However, while the World Health Organization (WHO) defines the normal or healthy BMI range as between 18.5 and 24.5 for the lowest risk of disease or mortality, studies have shown that the association between BMI and mortality risk varies based on ethnicity, population, and secular trends.

Although studies have reported a J-shaped curve for the association between BMI and mortality risk, recent research indicates that this association may not always apply based on the clinical context.

Furthermore, observational studies have shown that other adiposity markers that consider fat distribution and body composition, such as WHR and FMI, have stronger associations with disease and mortality risk than BMI.

About the study

In the present study, the researchers conducted a comparative analysis of the association between three adiposity markers and mortality risk using data from the United Kingdom (U.K.) Biobank.

Observational studies and Mendelian randomization analyses examined the association between mortality from specific causes, all-cause mortality, and FMI, WHR, and BMI.

The Mendelian randomization approach examines genetic variants while assessing the causal relationship between exposures and outcomes.

The researchers believe that an optimal measure or marker of clinical adiposity should not only show a strong association with adverse outcomes such as disease and mortality but also be easy to measure and consistent across a wide range of body compositions.

The U.K. Biobank population was divided into the discovery and validation cohorts for calculating polygenic risk scores and deriving adiposity measures determined by genetics using genome-wide association studies.

The validation cohort comprised cases of all-cause mortality with matched living controls based on sex, age, and genetic ancestry. The discovery cohort consisted of all the remaining Biobank participants.

The examined exposures included WHR, FMI, and BMI, which were calculated from anthropometric height and weight, circumference measurements of the hip and waist, and bioelectrical impedance analysis.

FMI is calculated as the ratio of fat mass to height, while WHR is a surrogate measure for abdominal adiposity.

The examined outcomes consisted of cancer, cardiovascular disease, respiratory disease, and all-cause mortality. Mortalities due to other diseases were also examined under a general category.

Information on an individual's genetic predisposition to a specific train, derived from the genetic variant's weighted effect on a phenotype, was used to calculate the polygenic risk scores.

The association between the adiposity markers and mortality outcomes was assessed using hazard models adjusted for age, sex, smoking habits, alcohol consumption, diabetes status, and other covariates.

The strength of the association between these adiposity markers and mortality was assessed using linear Mendelian randomization. In contrast, non-linear Mendelian randomization was used to assess the consistency of the adiposity markers.


The findings reported that WHR was the most consistent adiposity marker across a wide range of body compositions and showed the strongest association with cause-specific and all-cause mortality.

Furthermore, compared to FMI, WHR was easier to measure since it uses waist and hip circumference measurements and does not require bioelectrical impedance analysis.

The Mendelian randomization analyses also indicated that WHR had a causal relationship with mortality and could be used as a clinical marker for assessing mortality risk. Since weight loss is correlated to WHR, it can also be an appropriate intervention target.

Additionally, the Mendelian randomization results also showed that the association between WHR and mortality from cardiovascular disease and other diseases was also strong.


The findings suggested that WHR was a stronger adiposity marker than BMI for assessing all-cause mortality risk and was consistent across various body compositions.

WHR also showed strong associations with cause-specific mortality, including those related to cardiovascular disease. The results indicated that WHR could not only be used as a clinical marker for assessing mortality risk but also as an intervention target for weight loss.

Journal reference:
Dr. Chinta Sidharthan

Written by

Dr. Chinta Sidharthan

Chinta Sidharthan is a writer based in Bangalore, India. Her academic background is in evolutionary biology and genetics, and she has extensive experience in scientific research, teaching, science writing, and herpetology. Chinta holds a Ph.D. in evolutionary biology from the Indian Institute of Science and is passionate about science education, writing, animals, wildlife, and conservation. For her doctoral research, she explored the origins and diversification of blindsnakes in India, as a part of which she did extensive fieldwork in the jungles of southern India. She has received the Canadian Governor General’s bronze medal and Bangalore University gold medal for academic excellence and published her research in high-impact journals.


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