Body Composition in Individuals with Obesity According to Age and Sex: A Cross-Sectional Study

Maı̈moun, Laurent; Mura, Thibault; Avignon, A.; Mariano‐Goulart, Denis; Sultan, Ariane

doi:10.3390/jcm9041188

Cited by 4 publications

(9 citation statements)

References 41 publications

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“…All investigations and measurements are described in detail elsewhere 18 and were performed in fasting conditions in the morning (8:30–10 a.m.). The exclusion criteria were pregnancy, acute medical treatment, and any physical handicap (amputation, neurological lesion, orthopedic prosthesis) that might interfere with the body composition measurement.…”

Section: Methodsmentioning

confidence: 99%

“…and (ii) Are the cut-offs currently used appropriate for this population? The reduction in the ALMI(h 2 ) observed in these patients, which has been validated as a criterion for identifying the sarcopenia of aging 18 , points more to the possibility that the currently used cut-offs may be inappropriate. This also confirms the message from the EWGSOP2 and the ESPEN (European Society for Clinical Nutrition and Metabolism)/EASO (European Association for the Study of Obesity) statement, which underscores that sarcopenic obesity is a distinct condition 8 that needs a precise definition and adapted cut-offs 9 , 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Although few data are available 17 , it appears that subjects with obesity present a specific change in body composition with aging. For example, we recently reported that although women with obesity showed a localized redistribution of body FM and LTM with aging 18 , none presented sarcopenia when the cut-offs for appendicular lean mass index [appendicular lean mass/height 2 : ALMI(h 2 ) < 5.5 kg/m 2 for women] defined by the EWGSOP2 were applied 8 . Two questions arose from this observation: (i) Do patients with obesity develop sarcopenia?…”

Section: Introductionmentioning

confidence: 99%

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Definition of an adapted cut-off for determining low lean tissue mass in older women with obesity: a comparison to current cut-offs

Maı̈moun

Serrand²,

Mura³

et al. 2022

Sci Rep

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The prevalence of sarcopenia in patients with obesity varies according to the definition used. The purpose of our study was to: (i) determine the prevalence of sarcopenia in terms of lean tissue mass in older women with obesity using the current cut-offs, (ii) redefine a specific cut-off for low lean tissue mass (LLTM), and (iii) re-determine the prevalence of LLTM using this new cut-off. Appendicular lean mass (ALM) and the ALM index [ALM/height2: ALMI(h2)] and ALMI/body mass index [ALMI(BMI)] were determined in 791 women with or without obesity. LLMM prevalence was calculated using the current cut-offs: EWGSOP2: ALM < 15 kg and ALMI(h2) < 5.5 kg/m2; FNIH: ALM < 15.02 kg and ALMI(BMI) < 0.51; and IWGS: ALMI(h2) < 5.67 kg/m2 and cut-offs newly determined from data provided from young women with obesity. ALM, ALMI(h2) and ALMI(BMI) were lower in older compared to young obese women. Using the current cut-offs, a wide distribution of LLTM prevalence (0 to 29.2%) was observed. When the newly determined cut-offs were applied – i.e., ALM < 18.51 kg; ALMI(h2) < 7.15 kg/m2, ALMI(BMI) < 0.483, and T-score: [(ALMI(h2) measured)-(2.08 + 0.183*BMI)]/0.72] − the LLTM mass prevalence was 17.37%; 8.47, 14.8 and 12.71%. respectively. This study showed that the current cut-offs for LLTM as criteria for sarcopenia diagnosis are not adapted to the obese population. Although the new “static” cut-offs appeared to be more adapted, a “dynamic” cut-off for ALMI(h2) that took into account the BMI and thus the obesity severity appeared even more relevant.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Definition of an adapted cut-off for determining low lean tissue mass in older women with obesity: a comparison to current cut-offs

Maı̈moun

Serrand²,

Mura³

et al. 2022

Sci Rep

Self Cite

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“…Moreover, according to the same source, since 1975 the prevalence of obesity in the world has nearly tripled [1]. Beyond the BMI cut-off, the WHO originally defines overweight or obesity as an increased body fat (BF) accumulation in the adipose tissue, especially in central visceral regions [1,2]. Thus, the classification of adiposity based on BF quantification and assessment seems to be the most accurate [3], especially when, regardless of BMI, an increase in BF% is associated with major risk factors for cardiovascular diseases [4] as well as all-cause mortality [5].…”

Section: Introductionmentioning

confidence: 99%

“…BF% = predicted body fat percentage 2. Proportion of people with difference from measured BF% < 10% indicating underprediction 3.…”

mentioning

confidence: 99%

Development of an Easy-to-Use Prediction Equation for Body Fat Percentage Based on BMI in Overweight and Obese Lebanese Adults

et al. 2020

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An accurate estimation of body fat percentage (BF%) in patients who are overweight or obese is of clinical importance. In this study, we aimed to develop an easy-to-use BF% predictive equation based on body mass index (BMI) suitable for individuals in this population. A simplified prediction equation was developed and evaluated for validity using anthropometric measurements from 375 adults of both genders who were overweight or obese. Measurements were taken in the outpatient clinic of the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). A total of 238 participants were used for model building (training sample) and another 137 participants were used for evaluating validity (validation sample). The final predicted model included BMI and sex, with non-significant prediction bias in BF% of −0.017 ± 3.86% (p = 0.946, Cohen’s d = 0.004). Moreover, a Pearson’s correlation between measured and predicted BF% was strongly significant (r = 0.84, p < 0.05). We are presenting a model that accurately predicted BF% in 61% of the validation sample with an absolute percent error less than 10% and non-significant prediction bias (−0.028 ± 4.67%). We suggest the following equations: BF% females = 0.624 × BMI + 21.835 and BF% males = 1.050 × BMI − 4.001 for accurate BF% estimation in patients who are overweight or obese in a clinical setting in Lebanon.

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Comparison of the Discovery A and Stratos DR densitometers for assessing whole‐body and regional bone mineral density and body composition

Maı̈moun

Mahadea

Alonso

et al. 2023

Clin Physio Funct Imaging

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Purpose The agreement between the Stratos DR and Discovery A densitometers was assessed for measurements of whole‐body (WB) and regional fat mass (FM), fat‐free soft tissue (FFST) and bone mineral density (BMD). Moreover, the precision of the Stratos DR was also evaluated. Methods Fifty participants (35 women, 70%) were measured consecutively, once on the Discovery A and once on the Stratos DR. In a subgroup of participants (n = 29), two successive measurements with the Stratos DR were also performed. Results FM, FFST and BMD measured with the two devices were highly correlated, with a coefficient of correlation ranging from 0.80 to 0.99. Bland‐Altman analyses indicated significant bias between the two devices for all measurements. Thus, compared to the Discovery A, the Stratos DR underestimated WB BMD and WB and regional FM and FFST, with the exception of trunk FM and visceral adipose tissue (VAT), which were overestimated. Precision error for the Stratos DR, when expressed as root mean square‐coefficient of variation (RMS‐CV%) for FM, was 1.4% for WB, 3.0% for the gynoid and android regions, and 15.9% for VAT. The RMS‐CV% for FFST was 1.0% for WB. The root mean square of standard deviation for WB BMD was 0.018 g/cm², corresponding to a 1.4% CV. The least significant change was 0.050 g/cm² (SD), and 4.0% was considered to be a significant biological change. Conclusions Differences between the Stratos DR and Discovery A measurements are significant and require the use of translational cross‐calibration equations. For most of the BMD and body composition parameters, our results demonstrated good Stratos DR precision.

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Body Composition in Individuals with Obesity According to Age and Sex: A Cross-Sectional Study

Cited by 4 publications

References 41 publications

Definition of an adapted cut-off for determining low lean tissue mass in older women with obesity: a comparison to current cut-offs

Definition of an adapted cut-off for determining low lean tissue mass in older women with obesity: a comparison to current cut-offs

Development of an Easy-to-Use Prediction Equation for Body Fat Percentage Based on BMI in Overweight and Obese Lebanese Adults

Comparison of the Discovery A and Stratos DR densitometers for assessing whole‐body and regional bone mineral density and body composition

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