Obesity Among Postmenopausal Women

What Is the Best Anthropometric Index to Assess Adiposity and Success of Weight-loss Intervention?

Ahmed Ghachem, PhD; Alexis Marcotte-Chénard, MSc; Dominic Tremblay, MSc; Denis Prud'homme, MD, MSc; Rémi Rabasa-Lhoret, MD, PhD; Eléonor Riesco, PhD; Martin Brochu, PhD; Isabelle J. Dionne, PhD


Menopause. 2021;28(6):678-685. 

In This Article

Abstract and Introduction


Objectives: First, to establish the respective ability of body mass index (BMI), waist circumference (WC), and relative fat mass index (RFM), to estimate body fat (BF%) measured by DXA (DXA-BF%) and correctly identify postmenopausal women living with obesity (BF% > 35). Second, to identify the best indicator of successful weight-loss intervention in postmenopausal women living with obesity.

Methods: A total of 277 women (age: 59.8 ± 5.3 y; BF%: 43.4 ± 5.3) from five weight-loss studies with complete data for anthropometric measurements [BMI = weight/height (kg/m2); WC (cm)] and BF% were pooled together. Statistical performance indicators were determined to assess ability of RFM [64−(20 × height/waist circumference) + (12 × sex)], BMI and WC to estimate BF% before and after weight-loss intervention and to correctly identify postmenopausal women living with obesity.

Results: Compared with RFM (r = 0.51; r 2 = 0.27; RMSE = 4.4%; Lin's CCC = 0.46) and WC (r = 0.49; r 2 = 0.25; RMSE = 4.8%; Lin's CCC = 0.41), BMI (r = 0.73; r 2 = 0.52; RMSE = 3.7%; Lin's CCC = 0.71) was the best anthropometric index to estimate DXA-BF% and correctly identify postmenopausal women living with obesity (sensitivity + specificity: BMI = 193; RFM = 152; WC = 158), with lower misclassification error, before weight-loss intervention. After weight-loss, the change in BMI was strongly correlated with change in DXA-BF%, indicating that the BMI is the best indicator of success weight-loss intervention.

Conclusion: In the absence of more objective measures of adiposity, BMI is a suitable proxy measure for BF% in postmenopausal women, for whom a lifestyle intervention is relevant. Furthermore, BMI can be used as an indicator to assess success of weight-loss intervention in this subpopulation.


Excessive body fat accumulation is generally associated with deleterious cardiometabolic profile leading to type 2 diabetes, cardiovascular diseases, and premature death.[1] Menopause, a milestone in the life of women, is characterized by various changes in body composition,[2] particularly a significant total and central fat mass accumulation due to hormonal alterations such as a deficiency in estrogen production.[3] Thus, postmenopausal women are considered a subpopulation at risk of obesity and cardiometabolic issues, for whom a lifestyle intervention can be recommended.

Given that fat mass is involved in many cardiometabolic problems, the loss of fat mass is one major outcome of interest of weight-loss intervention and an indicator of the intervention's success. However, estimating body fat requires sophisticated techniques, expensive materials (bioelectrical impedance, dual-energy X-ray absorptiometry DXA, underwater weighing, air displacement, and skinfold thickness) and qualified personnel.[4,5] All make measuring body fat as part of weight-loss interventions, a common desired outcome, but not readily available.

Several anthropometric indexes were proposed over the past decades to estimate body fat percentage (BF%).[5] Because they are easy to interpret, accessible, inexpensive, and highly correlated with body fat (0.80 > r < 0.92,[6,7]), body mass index (BMI) and waist circumference (WC) are among the most common tools used in cohort and clinical studies, first, to identify individuals at higher risk of developing obesity-related disorders[5,8] and second, to assess the success of weight-loss interventions.[9]

Although widely used, BMI and WC remain indirect markers of BF% and thus, several studies are critical to its use in certain instances. For example, it has been mentioned that BMI remains a rough indicator of body fat that does not consider the effects of sex, age, and ethnicity on body composition, nor the distribution of fat mass.[10–12] Some studies also reported that WC taken at multiple sites (immediately below the lowest rib vs at the narrowest waist vs at the midpoint between the lowest rib and the iliac crest vs immediately above the iliac crest) can compromise the validity of the measure as well as its accuracy to estimate body fat.[13] All these limits make BMI and WC imprecise with potential high percentage of misclassification of obese individuals based on BF% (women > 35%, World Health Organization (WHO), 1995).[14,15] In this regard, Romero-Corral et al[14] showed that more than 50% of individuals having high BF% would be misclassified based on the BMI-defined obesity threshold (≥ 30 kg/m2). This misclassification was shown to be even higher in older adults.[14]

Recently, using a large sample from the US population, Woolcott and Bergman in 2018 proposed a new proxy of whole BF%, termed relative fat mass index (RFM). Calculated based on height, waist circumference, and sex, RFM was considered a simple and low-cost anthropometric index with a better performance to estimate BF% measured by DXA (DXA-BF%) compared with BMI.[16] The authors also concluded that RFM displayed better accuracy and significantly reduced obesity misclassification based on BF% in men and women from all ethnicity subgroups. Finally, given its simplicity and accuracy it was suggested that RFM could be used as a tool of choice to assess adiposity in the general population.[16] Thus, since its publication, the RFM has been frequently cited in the literature and several studies have aimed to validate its usefulness in subpopulations,[17–19] suggesting the growing interest to use such an indicator. However, findings from these studies proved to be inconsistent depending on sex, ethnicity, and age subgroups. The reasons for this discrepancy are not clear, and it is thus important to replicate results in additional subpopulations.

Thus, the main objective of the present study was to perform external validation of the equation of Woolcott and Bergman in 2018 to estimate body fat and evaluate the success to weight-loss interventions in postmenopausal women, a subpopulation greatly affected by obesity. Furthermore, our study extends the work of Woolcott et al[16] by validating the RFM compared to WC, a body fat measurement most used by health organizations to issue recommendations. We hypothesize that RFM is the best anthropometric index to correctly identify postmenopausal women living with obesity and better predict DXA-BF% before and after a weight-loss intervention compared with BMI and WC. We anticipate that results from this study will support clinicians to assess obesity and success in the context of weight-loss interventions in postmenopausal women.