An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity

Lilia V. Castro-Porras, Mario E. Rojas-Russell, Javier Villanueva-Sánchez, Malaquías López-Cervantes

Research output: Contribution to journalArticleResearchpeer-review

Abstract

© The Authors 2019. ObjectiveTo develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.DesignCross-sectional study.SettingMexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m 2 .ResultsThirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.ConclusionsThe anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.
Original languageAmerican English
JournalPublic Health Nutrition
DOIs
StatePublished - 1 Jan 2019

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Anthropometry
Obesity
Fats
Weights and Measures
Plethysmography
Epidemiologic Studies
Air
Population

Cite this

Castro-Porras, Lilia V. ; Rojas-Russell, Mario E. ; Villanueva-Sánchez, Javier ; López-Cervantes, Malaquías. / An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity. In: Public Health Nutrition. 2019.
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abstract = "{\circledC} The Authors 2019. ObjectiveTo develop a new predictive equation for fat mass percentage ({\%}FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.DesignCross-sectional study.SettingMexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m 2 .ResultsThirty-seven per cent of our sample was obese using {\%}FM measured by air-displacement plethysmography (BOD POD{\circledR}; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The {\%}FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 {\%} CI 86·8, 93·8) {\%} and a specificity of 58·0 (95 {\%} CI 52·1, 63·8) {\%} in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 {\%} of participants without obesity were correctly classified.ConclusionsThe anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the {\%}FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.",
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An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity. / Castro-Porras, Lilia V.; Rojas-Russell, Mario E.; Villanueva-Sánchez, Javier; López-Cervantes, Malaquías.

In: Public Health Nutrition, 01.01.2019.

Research output: Contribution to journalArticleResearchpeer-review

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N2 - © The Authors 2019. ObjectiveTo develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.DesignCross-sectional study.SettingMexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m 2 .ResultsThirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.ConclusionsThe anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

AB - © The Authors 2019. ObjectiveTo develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.DesignCross-sectional study.SettingMexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m 2 .ResultsThirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.ConclusionsThe anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

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