# Altered body composition in obesity: Prevalence, associated factors and comparison of two methods

Mathilde Fichet, Estelle Le Pabic, Laurence Lacaze, Mickaël Som, Emeric Rageul, Mathilde Barrallier, Mylène Guiho, Damien Bergeat, Ronan Thibault

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Mathilde Fichet, Estelle Le Pabic, Laurence Lacaze, Mickaël Som, Emeric Rageul, et al. Altered body composition in obesity: Prevalence, associated factors and comparison of two methods. Clinical Nutrition, 2025, 44, pp.147-154. 10.1016/j.clnu.2024.11.040. hal-04846566

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Original article

# Altered body composition in obesity: Prevalence, associated factors and comparison of two methods

<figure><img src="image_3.png" /></figure>

Mathilde Fichet<sup>a</sup>, Estelle Le Pabic<sup>b</sup>, Laurence Lacaze<sup>a, c</sup>, Mickaël Som<sup>a</sup>, Emeric Rageul<sup>a</sup>, Mathilde Barrallier<sup>a</sup>, Mylène Guiho<sup>a</sup>, Damien Bergeat<sup>d</sup>, Ronan Thibault<sup>a, c, *</sup>

## ARTICLE INFO

**Article history:**

Received 2 July 2024

Accepted 24 November 2024

**Keywords:**

Malnutrition

Sarcopenic obesity

Bioimpedance analysis

Chronic diseases

## SUMMARY

**Background & aims:** There is still some uncertainty towards the best method to diagnose the sarcopenic obesity, and specifically, altered body composition. A recent international consensus proposes the association of increased fat mass (FM) with reduced skeletal muscle mass adjusted by weight (SMM/W) (method 1). The ratio of fat mass index (FMI)/fat free mass index (FFMI) (method 2) has also been proposed. Aims: to determine and compare the prevalence of altered body composition by the two methods, and its associated factors, in obese patients referred to a specialized center.

**Methods:** Anthropometry, single-frequency bioimpedance analysis (BIA), and risk factors for sarcopenia were collected. SMM, according to the Janssen equation, and the FMI/FFMI ratio, were calculated. The agreement between the two methods was determined by the kappa coefficient. Multivariable analysis identified the factors associated with altered body composition according to the two methods.

**Results:** 253 consecutive patients were retrospectively included: 81 % women, age 44.4 ± 13 yr, body mass index (BMI) 43.0 ± 6.8 kg/m², severe obesity, 94 %. Altered body composition was reported in 90 % of the obese patients (n = 227/253) with method 1, and 35 % (83/253) with method 2. The agreement between the two methods was very low (kappa = 0.10). The factors associated with altered body composition were: method 1, age (odds ratio = 1.05 [95 % confidence interval, 1.01; 1.10], P = 0.02), arterial hypertension (0.18 [0.06; 0.57], P = 0.04), hip circumference (1.13 [1.07; 1.20], P < 0.0001), previous obesity follow-up (3.78 [1.30; 10.96], P = 0.002); method 2, female gender (3.12 [1.02; 9.5], P = 0.04), age (1.10 [1.06; 1.14], P < 0.0001), BMI (1.37 [1.26; 1.50], P < 0.0001), history of cancer (5.35 [1.20; 23.84], P < 0.028), phase angle (0.42 [0.27; 0.66], P < 0.001).

**Conclusion:** Altered body composition is common in severely obese patients. The two methods are not comparable. Further work will aim at refining the methods of body composition measurements in the obese patients, and especially for the sarcopenic obesity diagnosis.

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## 1. Introduction

Sarcopenic obesity is a clinical entity defined by the coexistence of obesity, characterized by an excess in fat mass, and sarcopenia [1]. Sarcopenia is defined by a decrease in muscle mass associated with a decrease in muscle function [2]. According to the consensus of the European Working Group on Sarcopenia in Older People 2

(EWGSOP2) revised in 2018 [2], primary sarcopenia is observed during aging. Secondary sarcopenia is that related to a lack of physical activity, malnutrition or inflammation of chronic diseases, including obesity [2], and could be observed at younger age.

Several expert groups have attempted in recent years to define sarcopenia and the thresholds of decreased muscle mass and function [3–5]. In young (<65 years) and severely obese patients (body mass index (BMI) ≥35), Sarcopenic obesity may be particularly difficult to define, because of the scarcity of data [6]. Yet, sarcopenic obesity could be associated with potentially more serious complications than sarcopenia or obesity itself. The risk of

<footnote><sup>a</sup> Department of Endocrinology-Diabetology-Nutrition, Home Parenteral Nutrition Centre, CHU Rennes, Rennes, France</footnote>

<footnote><sup>b</sup> CHU Rennes, INSERM, CIC 1414 (Clinical Investigation Centre), Rennes, France</footnote>

<footnote><sup>c</sup> INRAE, INSERM, Univ Rennes, NuMeCan, Nutrition Metabolisms Cancer, Rennes, France</footnote>

<footnote><sup>d</sup> Department of Digestive, Liver and Bile Tract Surgery, CHU Rennes, Rennes, France</footnote>

<footnote>* Corresponding author. Centre labellisé de nutrition parentérale au domicile - CHU Rennes, 2, rue Henri Le Guilloux, 35000 Rennes, France. E-mail address: ronan.thibault@chu-rennes.fr (R. Thibault).</footnote>
high blood pressure, metabolic syndrome and congestive heart failure would be higher in patients with sarcopenic obesity [7]. Other studies showed that the physical performance of women with sarcopenic obesity was significantly impaired [8] and patients with sarcopenic obesity were two to three times more likely to develop a disability in instrumental activities of daily living than sarcopenic or obese subjects [9]. Sarcopenic obesity is associated with a 24 % increase in all-cause mortality, higher than obesity alone [10]. It is therefore necessary to develop accurate methods to diagnose sarcopenic obesity, with the objective to better prevent and treat sarcopenic obesity.

Several diagnostic criteria to diagnose sarcopenic obesity have been proposed, making varying the prevalence of sarcopenic obesity from 4 to 90 % depending on the definition used [11,12]. Techniques assessing body composition, such as dual X-ray absorptiometry or bioimpedance analysis, are the cornerstone of the diagnosis of insufficient muscle mass [12,13]. Based on a systematic review of the literature [12], the European Society of Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO), propose to define the altered body composition of the obese patient as follows: increased fat mass (FM) associated with reduced skeletal muscle mass (SMM) (assessed by bioimpedance analysis (BIA)), adjusted by weight (W) (SMM/W) [13]. Moreover, they suggested that the diagnosis of sarcopenic obesity should only be considered in at-risk individuals, i.e., with an increased BMI or waist circumference and a clinical condition at risk for decreased muscle mass and/or function [13].

Previously, the ratio of fat mass (FM) index (FMI)/fat-free mass (FFM) index (FFMI) (FMI/FFMI ratio) has been proposed to define the decreased muscle mass of obese patients [14–17], but without defining clear diagnostic thresholds. Using cross-sectional data from NHANES III in 6372 patients. Xiao et al. defined cut-off values of the FMI/FFMI ratio for the diagnosis of sarcopenic obesity based on BIA measurements, according to age, sex, and BMI [18]. The FMI/FFMI ratio could be a relevant ratio taking into account muscle mass versus excess fat mas, but the ESPEN/EASO expert group has not considered this indicator as relevant in view of the currently insufficient clinical data [12].

Nevertheless, there is still some uncertainty towards the best method to diagnose the altered body composition in obesity, a key part towards the sarcopenic obesity diagnosis. Therefore, the aims of our study were to determine and compare the prevalence of the altered body composition in obesity, and its associated factors, using the two methods, i.e. SMM/W and FMI/FFMI ratio, in obese patients referred to our specialized tertiary center.

# 2. Patients and methods

## 2.1. Design and population

We conducted an observational, retrospective, cross-sectional study in consecutive obese patients consulting for the first time from January 1st, 2017 to December 31, 2019, in the Department of Endocrinology-Diabetology-Nutrition at Rennes University Hospital (CHU Rennes), Rennes, France, a referral tertiary centre for obesity. The inclusion criteria were: age ≥18 years and BMI ≥30 kg/m². The exclusion criteria were previous follow-up in our obesity centre before 2017. The Direction for Innovation and Clinical Research, CHU Rennes, Rennes, France, authorizes this retrospective research on April 8, 2024. Accordingly, all the patients were

informed that their data collected in the routine clinical practice could be analyzed anonymously for research.

## 2.2. Data collection

Demographic (age, sex) and anthropometric (height, weight, waist circumference, hip circumference) data, comorbidities, medical and psychological history, previous medical follow-up for obesity and previous history of obesity surgery, were collected retrospectively from the computerized medical record. BMI was calculated as the ratio of weight (kg) to height squared (m²). The following actual or past comorbidities were considered: cardiovascular disease (CVD) (coronary heart disease, stroke, carotid atheroma), high blood pressure, diabetes, obstructive sleep apnea syndrome (OSAS), chronic liver disease (steatosis, fibrosis or cirrhosis), cancer, chronic organ failure (heart, lung, kidney or liver), chronic obstructive pulmonary disease (COPD), asthma, hypothyroidism, inflammatory diseases, and mental disorders. The following drug therapies were collected: systemic or inhaled corticosteroids. A detailed history of addictive behaviors was collected: tobacco, alcohol, drugs, and eating disorders. The level of self-reported physical activity was classified into two subgroups: sedentary or any occasional/regular physical activity (minimum 20 min of daily activity).

Biological analyses were obtained from the laboratory reports: fasting blood glucose, triglycerides, LDL- and HDL-cholesterol, albumin, transthyretin, C reactive protein (CRP), liver function tests, zinc, vitamin B9, vitamin B12, 25-OH-vitamin D, hemoglobin. According to plasma 25-OH vitamin D concentrations, patients were classified into two categories: ≥30 ng/ml (no deficiency) and <30 ng/ml (deficiency). Metabolic syndrome was defined according to the National Cholesterol Education Program definition, as the presence of at least three of the following criteria: waist circumference ≥102 cm for men and ≥88 cm for women, triglycerides ≥1.7 mmol/l, HDL cholesterol <1.04 mmol/l for men and <1.3 mmol/l for women, fasting blood glucose ≥6.1 mmol/l, blood pressure ≥130/85 mmHg [19].

## 2.3. Diagnosis of altered body composition in obesity by bioimpedance analysis (BIA)

In our obesity referral centre, all patients were performed BIA for body composition assessment, as a routine exam. According to the ESPEN/EASO expert consensus [13], we first identified the patients reporting symptoms or risk factors for sarcopenia: age >70 years; chronic disease diagnosis (e.g., inflammatory diseases and organ failure or chronic disease) including but not limited to: heart failure, kidney disease (particularly renal replacement therapy), bowel failure or dysfunction, liver disease (particularly non-alcoholic steatohepatitis and liver cirrhosis), respiratory disease, neurologic and neurodegenerative diseases, chronic cognitive impairment, depression, organ transplantation, endocrine diseases (e.g., metabolic syndrome, diabetes mellitus, hypercortisolism, hypogonadism and corticoid treatment), osteoarthritis; cancer (especially but not limited to chemotherapy of breast or prostate cancer); recent acute disease/nutritional events: hospitalization (particularly but not limited to Coronavirus disease-19, intensive care unit stay, surgery), major surgery or trauma with/without complications, sustained immobilization or reduced mobility (e.g., trauma, fracture, orthopaedic disease), history of reduced food intake (e.g., <50 % for >2 weeks), recent weight loss, recent rapid
increase in weight; long-standing restrictive diets and obesity surgery; history – complaint of: repeated falls, weakness, exhaustion, fatigability, perceived progressive movement limitations. The two ratios (SMM/W and FMI/FFMI) (see below) were calculated in all the patients fulfilling these criteria.

The bioimpedance analyser (Bodystat 1500 MDD, Bodystat Ltd, Man Island, United Kingdom) was a single-frequency (50 kHz) impedancemeter, coupled with software allowing the transfer and recording of all measured data. We used the surface electrodes furnished by the Bodystat® company with the bioimpedance analyser. The measurement procedure followed the standard procedure and the manufacturer's instructions, and has been described elsewhere [20]. The BIA measured parameters were resistance (ohm), reactance X (ohm), impedance (ohm) and phase angle (degree). Then, the following body composition data were obtained: FM (kg and %), FFM (kg and %), and total body water (l and %). The FMI (=FM (kg)/height (m)$^2$), FFM (=FFM (kg)/height (m)$^2$), and the FMI/FFMI ratios were then calculated. The equations to calculate percent body fat and FFM have not been released by the manufacturer. SMM was calculated according to the Janssen equation [21]: SMM (kg) = [(height$^2$/BIA-resistance x 0.401) + (gender x 3.825) + (age x -0.071)] + 5.102, with height in cm; resistance in ohm; 1 for male gender and 0 for female gender; and age in years. The SMM/W was calculated and multiplied by 100 and reported as a percentage.

Altered body composition in obesity was defined by the association of excess of FM and pathological (SMM/W)*100 (method 1) or pathological FMI/FFMI ratio (method 2). FMI/FFMI ratio was considered as pathological in case of excess of FM according to Gallagher et al. [22] in function of age: 20–39 years of age (for 3 patients <20 years, we applied the same thresholds as for the population 20–39 years): >39 % for women, >26 % for men; 40–59 years of age: >41 % for women, >29 % for men; 60–79 years of age: >43 % for women, >31 % for men (SMM/W)*100 < 27.6 % for women or < 37 % for men, according to Janssen et al. [23]; FMI/FFMI ratio exceeding age-, sex-, and BMI-specific cutoff values corresponding to the 95th percentile of the distribution of this ratio derived from the National Health and Nutrition Examination Survey (NHANES) Database [18] (Table 1).

## 2.4. Statistical analyses

Statistical analyses were performed using SAS software, v.9.4® (SAS Institute, Cary, NC, USA). Two-sided statistical significance was set at $P < 0.05$. For data description, quantitative results were expressed as mean ± standard deviation and qualitative results were expressed as n and percentage. For group comparisons, the parametric Student test or the nonparametric Mann–Whitney or Wilcoxon tests were used for quantitative variables and the Chi2 test or Fisher exact test were used for qualitative variables. The kappa coefficient was calculated to determine the agreement between the two assessment methods of altered body composition, and was interpreted as follows: <0, disagreement; 0–0.2, very low

agreement; 0.21–0.4, low agreement; 0.41–0.6, moderate agreement; 0.61–0.8, strong agreement; 0.81–1.0 = near perfect agreement [24].

Factors associated with altered body composition in obesity were determined by logistic regression multivariable analysis and reported with the odds ratio (OR) and its 95 % confidence interval (CI). Variables significant at 0.20 as well as age and sex were included in the multivariable model and a stepwise top-down selection was performed.

# 3. Results

## 3.1. Description of the population

253 patients were included in the study: age, mean ± SD, 44.4 ± 13.0 yr, 7 patients (2.9 %) were ≥70 years, 204 women (80.6 %), BMI, mean ± SD, 43.0 ± 6.8 kg/m$^2$ [range 30.2–72.5], 238 patients (94.1 %) had severe obesity. Patients' characteristics are reported in Table 2. None of the patients had increased total body water, thus fluid overload.

## 3.2. Prevalence of altered body composition in obesity

Altered body composition in obesity was reported in 35.2 % of cases using method 1 (FMI/FFMI), and in 89.7 % using method 2 ((SMM/W) * 100) (Table 3).

## 3.3. Concordance between the two methods of altered body composition diagnosis

The agreement between the two methods was very low: kappa coefficient = 0.10. Only 44.7 % of patients were classified into the same category by the two methods. The SMM/W *100 method had detected all but one of the 89 patients with an increased FMI/FFMI ratio (Table 3).

## 3.4. Univariate and multivariable analysis of factors associated with altered body composition in obesity with method 1 (increased FM and reduced SMM/W*100)

In univariate analysis, only BMI and previous medical follow-up for obesity were significantly ($P < 0.05$) associated with altered body composition in obesity (Table 4). Variables included in the multivariable model with a $P < 0.20$ were age, gender, BMI, phase angle, arterial hypertension, diabetes, hypothyroidism, previous medical follow-up, HDL-cholesterol $≥1.04$ (male) or 1.3 (female) mmol/l, and zinc deficiency. After logistic regression, the factors associated with increased FM associated with reduced SMM/W were zinc deficiency, HDL-cholesterol $≥1.04$ (male) or 1.3 (female) mmol/l, arterial hypertension, body mass index, phase angle, and previous medical follow-up and history of obesity surgery (Table 5).

## 3.5. Univariate and multivariable analysis of factors associated with altered body composition in obesity with method 2 (FMI/FFMI ratio)

In univariate analysis, female gender, age, BMI, phase angle, previous history of cancer, hypertension, the level of physical activity, plasma HDL cholesterol concentration, and vitamin D deficiency were significantly ($P < 0.05$) associated with the FMI/FFMI ratio (Table 6). The logistic regression multivariable model showed that the factors associated with altered body composition in obesity were female gender, age, BMI, history of cancer, and phase angle (Table 7).

<table><caption>**Table 1**
Cutoff values at 95 % percentiles of the 'fat mass index (FMI)/fat-free mass index (FFMI) ratio' distribution derived from National Health and Nutrition Examination Survey Database [18].</caption><thead><tr><th rowspan="2">Age (years)</th><th colspan="2">18 to 39.9</th><th colspan="2">40 to 59.9</th><th colspan="2">60 to 69.9</th><th colspan="2">70 to 90</th></tr><tr><th>Men</th><th>Women</th><th>Men</th><th>Women</th><th>Men</th><th>Women</th><th>Men</th><th>Women</th></tr></thead><tbody><tr><td>30 to 39.9</td><td>0.57</td><td>1.01</td><td>0.59</td><td>0.93</td><td>0.59</td><td>0.97</td><td>0.61</td><td>0.95</td></tr><tr><td>≥40</td><td>0.86</td><td>1.17</td><td>0.79</td><td>1.10</td><td>0.66</td><td>1.02</td><td>0.69</td><td>1.12</td></tr></tbody></table>

BMI, body mass index.
<table><caption>**Table 2**
Demographic, anthropometry, and body composition data of the 253 included patients.</caption><thead><tr><th>Variables</th><th>Total population (n = 253)</th><th>Female (n = 204)</th><th>Male (n = 49)</th></tr></thead><tbody><tr><td><strong>Demography and anthropometry</strong></td><td></td><td></td><td></td></tr><tr><td>Gender: Male/female</td><td>49 (19.4)/204 (80.6)</td><td>—</td><td>—</td></tr><tr><td>Age, years</td><td>44.4 ± 13.0</td><td>44.5 ± 12.9</td><td>43.9 ± 13.3</td></tr><tr><td>Age, &lt;70/≥70 years</td><td>246 (97.2)/7 (2.8)</td><td>197 (96.6)/7 (3.4)</td><td>49 (100)/0</td></tr><tr><td>Waist circumference, cm</td><td>127.7 ± 14.7</td><td>125.5 ± 13.8</td><td>137.1 ± 14.6</td></tr><tr><td>Hip circumference, cm</td><td>130.8 ± 13.5</td><td>131.1 ± 13.8</td><td>129.3 ± 12.3</td></tr><tr><td>BMI, kg/m<sup>2</sup></td><td>43.0 ± 6.8</td><td>42.8 ± 7.0</td><td>43.7 ± 5.7</td></tr><tr><td>BMI, kg/m<sup>2</sup>, in class</td><td></td><td></td><td></td></tr><tr><td>30–34.9</td><td>15 (5.9)</td><td>12 (5.9)</td><td>3 (6.1)</td></tr><tr><td>35–39.9</td><td>88 (34.8)</td><td>76 (37.3)</td><td>12 (24.5)</td></tr><tr><td>≥40</td><td>150 (59.3)</td><td>116 (56.9)</td><td>34 (69.4)</td></tr><tr><td><strong>Body composition</strong></td><td></td><td></td><td></td></tr><tr><td>FM, kg</td><td>56.1 ± 14.9</td><td>56.6 ± 14.6</td><td>54.0 ± 15.7</td></tr><tr><td>FM, %</td><td>47.6 ± 7.1</td><td>49.6 ± 5.4</td><td>39.0 ± 6.7</td></tr><tr><td>FMI, kg/m<sup>2</sup></td><td>20.7 ± 5.8</td><td>21.5 ± 5.7</td><td>17.3 ± 4.7</td></tr><tr><td>FFM, kg</td><td>61.3 ± 13.1</td><td>56.4 ± 7.9</td><td>82.1 ± 9.9</td></tr><tr><td>FFM, %</td><td>52.5 ± 7.2</td><td>50.4 ± 5.4</td><td>61.3 ± 7.0</td></tr><tr><td>FFMI, kg/m<sup>2</sup></td><td>22.3 ± 2.9</td><td>21.3 ± 2.0</td><td>26.4 ± 2.6</td></tr><tr><td>Total body water (l)</td><td>46.8 ± 9.9</td><td>43.2 ± 6.3</td><td>61.6 ± 8.0</td></tr><tr><td>Total body water (%)</td><td>39.9 ± 4.7</td><td>38.6 ± 3.7</td><td>45.6 ± 3.9</td></tr><tr><td>Total body water (%)</td><td></td><td></td><td></td></tr><tr><td>≤40</td><td>137 (54.2)</td><td>134 (65.7)</td><td>3 (6.1)</td></tr><tr><td>[40–50]</td><td>108 (42.7)</td><td>69 (33.8)</td><td>39 (79.6)</td></tr><tr><td>[50–60]</td><td>8 (3.2)</td><td>1 (0.5)</td><td>7 (14.3)</td></tr><tr><td>Impedance, ohm</td><td>437.0 ± 75.2</td><td>451.8 ± 71.5</td><td>375.5 ± 57.1</td></tr><tr><td>Resistance, ohm</td><td>434.8 ± 76.6</td><td>449.7 ± 73.2</td><td>372.9 ± 57.0</td></tr><tr><td>Réactance, ohm</td><td>49.2 ± 9.5</td><td>49.4 ± 9.1</td><td>48.4 ± 11.3</td></tr><tr><td>Phase angle, degree</td><td>6.5 ± 1.3</td><td>6.3 ± 1.0</td><td>7.5 ± 1.7</td></tr></tbody></table>

BMI, body mass index; FM, fat mass; FMI, fat mass index; FFM, fat-free mass; FFMI, fat-free mass index. Qualitative variables are presented as n (%) and quantitative variables as mean ± standard deviation.

<table><caption>**Table 3**
Prevalence of altered body composition in obesity using the FMI/FFMI ratio (method 1) or increased FM and reduced SMM/W*100 (method 2) in the 253 included patients.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Increased FMI/FFMI ratio</th><th rowspan="2">Total</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td><strong>Increased FM and decreased SMM/W ratio</strong></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>88 (34.8 %)</td><td>139 (54.9 %)</td><td>227</td></tr><tr><td>No</td><td>1 (0.4 %)</td><td>25 (9.9 %)</td><td>26</td></tr><tr><td>Total</td><td>89</td><td>164</td><td>253</td></tr></tbody></table>

FM, fat mass; FFMI, fat-free mass index; FMI, fat mass index; SMM, skeletal muscle mass; SMM/W, skeletal muscle mass adjusted to weight; W, weight. Data are expressed as numbers (%).

## 4. Discussion

Our study indicates that the prevalence of altered body composition in obesity was different according to the method of body composition assessment used: ranging from 35.2 % using the FMI/FFMI ratio to 90 % using the increased FM and reduced SMM/W*100 as recently advised by the ESPEN/EASO consensus [13]. The agreement between these two methods was very poor. As expected, the factors associated with altered body composition in obesity differ between the two methods: zinc deficiency, HDL-cholesterol ≥1.04 (male) or 1.3 (female) mmol/l, arterial hypertension, BMI, phase angle, and previous medical follow-up and history of obesity surgery, with the increased FM and reduced SMM/W*100 method, age, gender, BMI, history of cancer, and phase angle, with the FMI/FFMI ratio.

The prevalence of altered body composition in obesity was very different according to the method used, and with little agreement between the two methods. This again suggests the variability of the prevalence of sarcopenic obesity depending on the method, the thresholds, and the study population [11]. For instance, a

retrospective study found that the prevalence of sarcopenic obesity according to ESPEN/EASO criteria in candidates for obesity surgery was 13 %–23 % based on the diagnostic method and cut-off points used [25]. Nevertheless, in our study, we could not conclude about the prevalence of sarcopenic obesity *per se*, as muscle mass only, but not muscle function, was assessed in this study.

The recent expert ESPEN/EASO consensus published in 2021 [13] used the literature data, and in particular the systematic review published by Donini et al. [12] to propose a diagnostic approach of sarcopenic obesity. The majority of studies (sixty-six out of seventy-five) were based on the coexistence of obesity and sarcopenia, considered as two clinical entities, to define sarcopenic obesity. Only two studies out of seventy-five used the association of an insufficient FFM according to the excess of fat mass using either the FM to FFM ratio [17] or the visceral adipose tissue area to thigh muscle area ratio [14]. Arguing the insufficiency of data, the expert consensus did not retain the use of the FM/FFM ratio to diagnose sarcopenic obesity.

Yet, a tool to quantify insufficient FFM with regard to excessive fat mass seems essential to identify patients with sarcopenic obesity, since there is a large variability in body composition among patients with identical BMI [16]. Despite high body weight, obese individuals may have low muscle mass or muscle dysfunction [26]. During periods of weight gain or loss, loss of muscle mass and increase in fat mass may be two concomitant phenomena. It could result in decreased physical activity, then an increased risk of functional impairment, leading to a vicious circle of sedentarity and muscle mass loss [27]. The FMI/FFMI ratio, taking into account the joint effects of FM and FFM variation, could therefore be a relevant index to identify a sarcopenic obesity-specific phenotype. Our study suggests this hypothesis since the prevalence of insufficient muscle mass was much lower with the FMI/FFMI ratio (35 %) than with the SMM/W*100 (90 %), and the concordance between the two was very low (kappa coefficient = 0.10). These findings suggest
<caption>**Table 4**</caption>

Univariate analysis of factors associated with altered body composition (ABC) in obesity with the increased FM and reduced SMM/W*100 method in the 253 included patients.

<table><thead><tr><th>Variables</th><th>Total<br>(n = 253)</th><th>No ABC<br>(n = 26)</th><th>ABC<br>(n = 227)</th><th>Odds ratio [95%CI]</th><th>P-value</th></tr></thead><tbody><tr><td><strong>Demography and anthropometry</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td>0.29</td></tr><tr><td>M</td><td>49 (19.4)</td><td>3 (11.5)</td><td>46 (20.3)</td><td>1</td><td></td></tr><tr><td>F</td><td>204 (80.6)</td><td>23 (88.5)</td><td>181 (79.7)</td><td>0.51 [0.15; 1.78]</td><td></td></tr><tr><td>Age, years</td><td>44.4 ± 13.0</td><td>43.3 ± 14.1</td><td>44.5 ± 12.9</td><td>1.01 [0.98; 1.04]</td><td>0.64</td></tr><tr><td>BMI, kg/m<sup>2</sup></td><td>43.0 ± 6.8</td><td>38.1 ± 4.8</td><td>43.6 ± 6.7</td><td>1.25 [1.12; 1.41]</td><td>0.01</td></tr><tr><td>Phase angle, degree</td><td>6.5 ± 1.3</td><td>7.0 ± 0.9</td><td>6.5 ± 1.3</td><td>0.78 [0.60; 1.02]</td><td>0.07</td></tr><tr><td><strong>Comorbidities</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CVD</td><td>19 (7.5)</td><td>1 (3.8)</td><td>18 (7.9)</td><td>2.15 [0.28; 16.82]</td><td>0.46</td></tr><tr><td>Arterial hypertension</td><td>90 (35.6)</td><td>13 (50)</td><td>77 (33.9)</td><td>0.51 [0.23; 1.16]</td><td>0.12</td></tr><tr><td>Diabetes</td><td>45 (17.8)</td><td>8 (30.8)</td><td>37 (16.3)</td><td>0.44 [0.18; 1.08]</td><td>0.07</td></tr><tr><td>OSAS</td><td>169 (66.8)</td><td>18 (69.2)</td><td>151 (66.5)</td><td>1.12 [0.46; 2.71]</td><td>0.97</td></tr><tr><td><strong>Chronic liver disease</strong></td><td></td><td></td><td></td><td></td><td>0.99</td></tr><tr><td>Steatosis</td><td>9 (3.6)</td><td>0 (0)</td><td>9 (4)</td><td>20.3440.8 [0.0; 1]</td><td></td></tr><tr><td>Fibrosis/cirrhosis</td><td>30 (11.9)</td><td>3 (11.5)</td><td>27 (11.9)</td><td>1.08 [0.30; 3.85]</td><td></td></tr><tr><td>Cancer</td><td>14 (5.5)</td><td>1 (3.8)</td><td>13 (5.7)</td><td>1.52 [0.19; 12.1]</td><td>0.69</td></tr><tr><td>Organ failure</td><td>10 (4)</td><td>1 (3.8)</td><td>9 (4.0)</td><td>1.03 [0.13; 8.48]</td><td>0.98</td></tr><tr><td>COPD</td><td>3 (1.2)</td><td>0 (0)</td><td>3 (1.3)</td><td>19.6096.1 [0.0; 1]</td><td>0.99</td></tr><tr><td>Asthma</td><td>38 (15.0)</td><td>2 (7.7)</td><td>36 (15.9)</td><td>2.26 [0.51; 9.99]</td><td>0.28</td></tr><tr><td>Hypothyroidism</td><td>34 (13.4)</td><td>1 (3.8)</td><td>33 (14.5)</td><td>4.25 [0.56; 32.4]</td><td>0.16</td></tr><tr><td>Inflammatory diseases</td><td>16 (6.3)</td><td>7 (4.3)</td><td>9 (10.1)</td><td>0.79 [0.17; 3.68]</td><td>0.76</td></tr><tr><td>Corticosteroids</td><td>19 (7.5)</td><td>0 (0)</td><td>19 (8.4)</td><td>21.1180.4 [0.0; 1]</td><td>0.97</td></tr><tr><td>Psychiatric history</td><td>66 (26.1)</td><td>6 (23.1)</td><td>60 (26.4)</td><td>1.20 [0.46; 3.12]</td><td>0.71</td></tr><tr><td><strong>Lifestyle</strong></td><td></td><td></td><td></td><td></td><td>0.99</td></tr><tr><td>Tobacco</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>69 (29.5)</td><td>6 (28.6)</td><td>63 (29.6)</td><td>1.05 [0.36; 3.03]</td><td></td></tr><tr><td>Yes</td><td>55 (23.5)</td><td>5 (23.8)</td><td>50 (23.5)</td><td>1.00 [0.32; 3.08]</td><td></td></tr><tr><td>Alcohol</td><td></td><td></td><td></td><td></td><td>0.65</td></tr><tr><td>Casual</td><td>98 (38.7)</td><td>12 (46.2)</td><td>86 (37.9)</td><td>0.75 [0.32; 1.71]</td><td></td></tr><tr><td>Daily</td><td>17 (6.7)</td><td>1 (3.8)</td><td>16 (7)</td><td>1.66 [0.20; 13.5]</td><td></td></tr><tr><td>Addiction</td><td>12 (4.7)</td><td>0 (0)</td><td>12 (5.3)</td><td>20.4304.8 [0.0; 1]</td><td>0.97</td></tr><tr><td>Eating disorders</td><td>148 (58.5)</td><td>13 (50)</td><td>135 (59.5)</td><td>1.47 [0.65; 3.31]</td><td>0.36</td></tr><tr><td>Physical activity</td><td>141 (55.7)</td><td>13 (50)</td><td>128 (56.4)</td><td>1.29 [0.57; 2.91]</td><td>0.53</td></tr><tr><td>Obesity therapy</td><td></td><td></td><td></td><td></td><td>0.01</td></tr><tr><td>Medical follow-up</td><td>149 (58.9)</td><td>7 (26.9)</td><td>142 (62.6)</td><td>3.43 [1.29; 9.08]</td><td></td></tr><tr><td>Obesity surgery</td><td>21 (8.3)</td><td>7 (26.9)</td><td>14 (6.2)</td><td>0.34 [0.11; 1.01]</td><td></td></tr><tr><td><strong>Biological tests</strong></td><td></td><td></td><td></td><td></td><td>0.69</td></tr><tr><td>Fasting blood glucose ≥6.1 mmol/l</td><td>60 (23.7)</td><td>7 (26.9)</td><td>53 (23.3)</td><td>0.83 [0.33; 2.07]</td><td>0.69</td></tr><tr><td>Triglycerides ≥1.7 mmol/l</td><td>82 (32.4)</td><td>9 (34.6)</td><td>73 (32.2)</td><td>0.90 [0.38; 2.10]</td><td>0.79</td></tr><tr><td><strong>HDL-cholesterol:</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>M ≥ 1.04 mmol/l; F ≥ 1.3 mmol/l</td><td>153 (60.5)</td><td>20 (76.9)</td><td>133 (58.6)</td><td>2.36 [0.91; 6.09]</td><td>0.08</td></tr><tr><td>Metabolic syndrome</td><td>116 (45.8)</td><td>12 (46.2)</td><td>104 (45.8)</td><td>0.99 [0.44; 2.23]</td><td>0.97</td></tr><tr><td>Albumin &lt;35 g/l</td><td>3 (1.2)</td><td>0 (0)</td><td>3 (1.3)</td><td>19.6251.4 [0.0; 1]</td><td>0.99</td></tr><tr><td>Transthyretin &lt;0.2 g/l</td><td>144 (57.8)</td><td>13 (50)</td><td>131 (58.7)</td><td>1.42 [0.63; 3.21]</td><td>0.39</td></tr><tr><td>Missing data (n)</td><td>4</td><td>0</td><td>4</td><td>—</td><td>—</td></tr><tr><td>CRP &gt;5 mg/l</td><td>141 (58.3)</td><td>12 (57.1)</td><td>129 (58.4)</td><td>1.05 [0.43; 2.60]</td><td>0.91</td></tr><tr><td>Elevated transaminases</td><td>77 (30.7)</td><td>9 (34.6)</td><td>68 (30.2)</td><td>0.82 [0.35; 1.93]</td><td>0.65</td></tr><tr><td>Elevated GGT</td><td>85 (33.7)</td><td>9 (34.6)</td><td>76 (33.6)</td><td>0.96 [0.41; 2.25]</td><td>0.91</td></tr><tr><td>Anemia</td><td>15 (5.9)</td><td>2 (7.7)</td><td>13 (5.7)</td><td>0.73 [0.16; 3.42]</td><td>0.69</td></tr><tr><td>Missing data (n)</td><td>16</td><td>6</td><td>10</td><td>—</td><td>—</td></tr><tr><td>Zinc deficiency</td><td>79 (31.7)</td><td>4 (16)</td><td>75 (33.5)</td><td>2.64 [0.88; 7.97]</td><td>0.08</td></tr><tr><td>Missing data (n)</td><td>4</td><td>1</td><td>3</td><td>—</td><td>—</td></tr><tr><td>Vitamin B9 deficiency</td><td>89 (35.3)</td><td>9 (34.6)</td><td>80 (35.4)</td><td>1.03 [0.44; 2.43]</td><td>0.94</td></tr><tr><td>Missing data (n)</td><td>1</td><td>0</td><td>1</td><td>—</td><td>—</td></tr><tr><td>Vitamin B12 deficiency</td><td>49 (19.4)</td><td>7 (26.9)</td><td>42 (18.5)</td><td>0.62 [0.24; 1.56]</td><td>0.31</td></tr><tr><td>Vitamin D deficiency</td><td>227 (89.7)</td><td>23 (88.4)</td><td>204 (89.9)</td><td>1.16 [0.32; 4.15]</td><td>0.82</td></tr></tbody></table>

BMI, body mass index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; CVD, cardiovascular disease; F, female; FM, fat mass; FFMI, fat-free mass index; FMI, fat mass index; GGT, gamma-glutamyl transpeptidase; HDL, high density lipoprotein; LDL, low density lipoprotein; M, male; OSAS, obstructive sleep apnea syndrome; SMM, skeletal muscle mass; SMM/W, skeletal muscle mass adjusted to weight; TSH, thyroid stimulating hormone; W, weight. Data are presented as n (%) with n, number of patients, or mean ± standard deviation.

that the SMM/W*100 could overestimate the prevalence of the altered body composition in obesity.

None of our patients had increased total body water. Thus, this is very unlikely that fluid overload could act as a confounding factor. However, in the present study, we used single-frequency (50 kHz) impedancemeter, thus we could not assess extra- (ECW) and intra- cellular (ICW) waters. There, we could not strictly exclude that altered ECW/ICW or ECW/TBW ratios could have influenced our results [28].

From a functional point of view, the adiposity/muscle ratio could predict the onset or worsening of physical limitation, especially in women over >65 years of age [14]. Others have demonstrated a faster walking speed and a lower probability of functional limitation in people over 55 years of age with a high FMI/FFMI ratio [29]. The FMI/FFMI ratio is therefore a tool to better understand the impact on health of both an increase in FM and a decrease in FFM. This remains to be demonstrated with the FM excess combined with the SMM/W*100 method.
<caption>**Table 5**</caption>

Multivariable analysis of factors associated with altered body composition in obesity with the increased fat mass (FM) and reduced skeletal muscle mass adjusted to weight (SMM/W)*100 method.

<table><thead><tr><th>Variables</th><th>Odds ratio [95 % CI]</th><th>P-value</th></tr></thead><tbody><tr><td>Zinc deficiency (yes vs no)</td><td>4.78 [1.17; 19.48]</td><td>0.03</td></tr><tr><td>HDL-cholesterol: M ≥ 1.04 mmol/l; F ≥ 1.3 mmol/l (yes vs no)</td><td>5.04 [1.38; 18.34]</td><td>0.01</td></tr><tr><td>Arterial hypertension (yes vs no)</td><td>0.30 [0.11; 0.85]</td><td>0.02</td></tr><tr><td>Body mass index (kg/m²) – for 1-point increase</td><td>1.26 [1.10; 1.44]</td><td>0.0007</td></tr><tr><td>Phase angle (°) – for 1-point increase</td><td>0.62 [0.42; 0.92]</td><td>0.02</td></tr><tr><td>Obesity therapy (reference = no)</td><td>1</td><td></td></tr><tr><td>Previous medical follow-up (yes vs no)</td><td>3.78 [1.30; 10.96]</td><td>0.0005</td></tr><tr><td>Obesity surgery (yes vs no)</td><td>0.12 [0.02; 0.68]</td><td></td></tr></tbody></table>

CI, confidence interval.

The threshold values used to define a pathological FMI/FFMI ratio remain debated since there is little data in the literature. For example, Biolo et al., who also used the FMI/FFMI ratio in BIA to assess sarcopenic obesity, used an FMI/FFMI threshold >0.8 [17], derived from the metabolic load and capacity model proposed by Prado et al.; in the latter, FMI/FFMI thresholds are set according to metabolic phenotype ("normal" phenotype, "adipose obesity," "sarcopenic obesity," "metabolically healthy obesity," and "muscle hypertrophy") [1]. The thresholds proposed by Xiao et al., corresponding to the 95th percentile of the distribution of the FMI/FFMI ratio derived from the NHANES III [18], have the advantage of having been established specifically according to age, sex and BMI, which are essential parameters to be considered when assessing body composition. To avoid potential confounding or misinterpretation, the cut-off values for the FMI/FFMI ratio used in our study were defined according to certain categories of age, gender, and BMI [18]. Our multivariable analysis indicates that these three variables were nevertheless significantly associated with the altered body composition in obesity assessed with the FMI/FFMI ratio. This suggests a relationship between these parameters and the altered body composition in obesity.

In our study, cancer, known to be a disease frequently associated with malnutrition [30], is strongly associated with the altered body composition in obesity diagnosed with the FMI/FFMI ratio. Phase angle was also associated with altered body composition in obesity whatever the assessment method used. This result supports data from the literature indicating a correlation between phase angle and malnutrition/sarcopenia. In a recent systematic review that included 13 studies, five of which involved subjects under 65 years of age, sarcopenia was associated with a significantly lower phase angle in seven of eight studies, while five of six studies reported a high prevalence of sarcopenia in patients with a low phase angle. Importantly, phase angle and sarcopenia were already identified as independent predictors of survival in cancer patients and older geriatric inpatients [31]. We also found zinc deficiency as a risk factor for altered body composition in obesity. This could be related to malnutrition/sarcopenia, as zinc deficiency is regu reported in patients with malnutrition and obesity [32].

Our findings of the factors associated with altered body composition in obesity with the excess FM together with insufficient SMM/W*100 ratio should be interpreted with caution given the small number of patients with normal FM and SMM/W ratio (n = 26 patients).

In multivariable analysis, one of the factors associated with altered body composition in obesity was age, a well-known risk factor for sarcopenia [5]. Other more unexpected factors were found: arterial hypertension as a protective factor, HDL-cholesterol above the metabolic syndrome threshold, and previous medical follow-up for obesity as risk factors. The number of years with obesity was not collected, but we could hypothesize that it is likely that patients reporting a previous medical follow-up for obesity

had lived more years with obesity, then being more at risk for sarcopenic obesity. A previous history of obesity surgery is associated with a lower risk of altered body composition. This is likely because those patients had lower fat mass, which is in line with our finding that BMI is a risk factor for altered body composition in obesity.

The main limitation of our study is the absence of assessment of muscle function, a key criterion for the diagnosis of sarcopenia [5]. Therefore, we could not conclude on the prevalence of sarcopenic obesity, and need to use the wording 'altered body composition in obesity'. According to the recent ESPEN-EASO consensus [13], the evaluation of muscle function is the first step in the diagnostic process of sarcopenic obesity and its alteration is required to continue the diagnostic process by measuring muscle mass. This could clearly explain the high prevalence of altered body composition in obesity that we reported by using the excessive FM associated with insufficient SMM/W*100 ratio. Undoubtedly, our population and results would have been different if muscle function had been considered. However, no study defining sarcopenia by the coexistence of both reduced strength and muscle mass was included in the recent systematic review by Donini et al. [12], on which was based the ESPEN/EASO consensus. Some studies have already used the low muscle strength only (without body composition assessment) to define the prevalence of sarcopenic obesity (32,8 %) [33], and some authors have used the wording "dynapenic obesity" [34] to define this condition. These clearly indicate the need of studies to validate a 100 % reliable method for diagnosing sarcopenic obesity.

As with all cross-sectional studies, no causal relationship can be drawn from our study. The declarative nature of certain variables, notably physical activity, eating disorders and lifestyle habits, needs to moderate the result interpretation because of a potential reporting bias. We can not exclude small differences between Bodystat 1500 MDD and RJL BIA 101 B impedancemeter devices (the latter was used for the validation of the Janssen equation [21]) when applying the Janssen equation [35]. Nevertheless, as we used the same methodology and the same device all along our study, our data remain comparable and justify our conclusions of the variability of altered body composition among obese patients by using the two different methods. As reported by others [36], the equations that we used to calculate percent body fat and fat-free mass to assess the FMI/FFMI ratio, have not been released by the manufacturer.

In conclusion, our study indicates a lack of agreement between two methods for assessing altered body composition in obesity, i.e. insufficient FMI/FFMI ratio and increased FM associated with insufficient SMM/W*100 ratio, as part of the sarcopenic obesity diagnosis, in a population of severely obese patients with risk factors for sarcopenia. Consequently, the prevalence of altered body composition in obesity and the associated factors differ, except for age. The ESPEN/EASO 2021 expert consensus on the diagnosis of
<caption>**Table 6**</caption>

Univariate analysis of factors associated with altered body composition in obesity (ABC) with the FMI/FFMI ratio in the 253 included patients.

<table><thead><tr><th>Variables</th><th>Total (n = 253)</th><th>No ABC (n = 164)</th><th>ABC (n = 89)</th><th>Odds ratio [95%CI]</th><th>P-value</th></tr></thead><tbody><tr><td>Demography and anthropometry</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td>0.04</td></tr><tr><td>M</td><td>49 (19.4)</td><td>38 (23.2)</td><td>11 (12.4)</td><td>1</td><td></td></tr><tr><td>F</td><td>204 (80.6)</td><td>126 (76.8)</td><td>78 (87.6)</td><td>2.14 [1.03; 4.43]</td><td></td></tr><tr><td>Age, years</td><td>44.4 ± 13.0</td><td>41.4 ± 12.2</td><td>50.0 ± 12.6</td><td>1.06 [1.03; 1.08]</td><td>&lt;0.0001</td></tr><tr><td>BMI, kg/m<sup>2</sup></td><td>43.0 ± 6.8</td><td>40.6 ± 4.5</td><td>47.5 ± 7.8</td><td>1.22 [1.15; 1.29]</td><td>&lt;0.0001</td></tr><tr><td>Phase angle, degree</td><td>6.5 ± 1.3</td><td>6.8 ± 1.3</td><td>6.1 ± 1.1</td><td>0.54 [0.41; 0.72]</td><td>&lt;0.0001</td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CVD</td><td>19 (7.5)</td><td>12 (7.3)</td><td>7 (7.9)</td><td>1.08 [0.41; 2.85]</td><td>0.87</td></tr><tr><td>Arterial hypertension</td><td>90 (35.6)</td><td>51 (31.1)</td><td>39 (43.8)</td><td>1.73 [1.01; 2.95]</td><td>0.04</td></tr><tr><td>Diabetes</td><td>45 (17.8)</td><td>28 (17.1)</td><td>17 (19.1)</td><td>1.15 [0.59; 2.23]</td><td>0.69</td></tr><tr><td>OSAS</td><td>169 (66.8)</td><td>112 (68.3)</td><td>57 (64.0)</td><td>0.88 [0.49; 1.57]</td><td>0.69</td></tr><tr><td>Chronic liver disease</td><td></td><td></td><td></td><td></td><td>0.39</td></tr><tr><td>Steatosis</td><td>9 (3.6)</td><td>8 (4.9)</td><td>1 (1.1)</td><td>0.24 [0.03; 1.96]</td><td></td></tr><tr><td>Fibrosis/cirrhosis</td><td>30 (11.9)</td><td>19 (11.6)</td><td>11 (12.4)</td><td>1.11 [0.51; 2.46]</td><td></td></tr><tr><td>Cancer</td><td>14 (5.5)</td><td>4 (2.4)</td><td>10 (11.2)</td><td>5.06 [1.54; 16.6]</td><td>0.01</td></tr><tr><td>Organ failure</td><td>10 (4.0)</td><td>5 (3.0)</td><td>5 (5.6)</td><td>1.89 [0.53; 6.72]</td><td>0.32</td></tr><tr><td>COPD</td><td>3 (1.2)</td><td>1 (0.6)</td><td>2 (2.2)</td><td>3.74 [0.33; 41.8]</td><td>0.28</td></tr><tr><td>Asthma</td><td>38 (15.0)</td><td>28 (17.1)</td><td>10 (11.2)</td><td>0.61 [0.28; 1.33]</td><td>0.22</td></tr><tr><td>Hypothyroidism</td><td>34 (13.4)</td><td>17 (10.4)</td><td>17 (19.1)</td><td>2.04 [0.98; 4.23]</td><td>0.06</td></tr><tr><td>Inflammatory diseases</td><td>16 (6.3)</td><td>7 (4.3)</td><td>9 (10.1)</td><td>2.52 [0.91; 7.02]</td><td>0.08</td></tr><tr><td>Corticosteroids</td><td>19 (7.5)</td><td>11 (6.7)</td><td>8 (8.9)</td><td>1.37 [0.53; 3.55]</td><td>0.51</td></tr><tr><td>Psychiatric history</td><td>66 (26.1)</td><td>39 (23.8)</td><td>27 (30.3)</td><td>1.40 [0.78; 2.49]</td><td>0.26</td></tr><tr><td>Lifestyle</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tobacco</td><td></td><td></td><td></td><td></td><td>0.90</td></tr><tr><td>No</td><td>69 (29.5)</td><td>45 (30.0)</td><td>24 (28.6)</td><td>0.83 [0.44; 1.55]</td><td></td></tr><tr><td>Yes</td><td>55 (23.5)</td><td>38 (25.3)</td><td>17 (20.2)</td><td>0.70 [0.35; 1.39]</td><td></td></tr><tr><td>Alcohol</td><td></td><td></td><td></td><td></td><td>0.57</td></tr><tr><td>Casual</td><td>98 (38.7)</td><td>71 (43.3)</td><td>27 (30.3)</td><td>0.51 [0.30; 0.88]</td><td></td></tr><tr><td>Daily</td><td>17 (6.7)</td><td>13 (7.9)</td><td>4 (4.5)</td><td>0.45 [0.14; 1.44]</td><td></td></tr><tr><td>Addiction</td><td>12 (4.7)</td><td>7 (4.3)</td><td>5 (5.6)</td><td>1.34 [0.41; 4.34]</td><td>0.63</td></tr><tr><td>Eating disorders</td><td>148 (58.5)</td><td>99 (60.4)</td><td>49 (55.1)</td><td>0.80 [0.48; 1.36]</td><td>0.41</td></tr><tr><td>Physical activity</td><td>141 (55.7)</td><td>100 (61.0)</td><td>41 (46.1)</td><td>0.55 [0.32; 0.92]</td><td>0.02</td></tr><tr><td>Obesity therapy</td><td></td><td></td><td></td><td></td><td>0.92</td></tr><tr><td>Medical follow-up</td><td>149 (58.9)</td><td>96 (58.5)</td><td>53 (59.6)</td><td>1.08 [0.62; 1.91]</td><td></td></tr><tr><td>Obesity surgery</td><td>21 (8.3)</td><td>13 (7.9)</td><td>8 (9.0)</td><td>1.21 [0.45; 3.26]</td><td></td></tr><tr><td>Biological tests</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fasting blood glucose, ≥6.1 mmol/l</td><td>60 (23.7)</td><td>36 (22)</td><td>24 (27)</td><td>1.31 [0.72; 2.38]</td><td>0.37</td></tr><tr><td>Triglycerides ≥1.7 mmol/l</td><td>82 (32.4)</td><td>57 (34.8)</td><td>25 (28.1)</td><td>0.73 [0.42; 1.29]</td><td>0.28</td></tr><tr><td>HDL-cholesterol:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>M &lt; 1.04 mmol/l; F &lt; 1.3 mmol/l</td><td>153 (60.5)</td><td>103 (62.8)</td><td>50 (56.2)</td><td>1.32 [0.78; 2.23]</td><td>0.30</td></tr><tr><td>Metabolic syndrome</td><td>116 (45.8)</td><td>75 (45.7)</td><td>41 (46.1)</td><td>1.01 [0.60; 1.70]</td><td>0.96</td></tr><tr><td>Albumin &lt;35 g/l</td><td>3 (1.2)</td><td>1 (0.6)</td><td>2 (2.2)</td><td>3.72 [0.33; 41.60]</td><td>0.29</td></tr><tr><td>Transthyretin &lt;0.2 g/l</td><td>144 (57.8)</td><td>87 (53.7)</td><td>57 (65.5)</td><td>1.64 [0.96; 2.81]</td><td>0.07</td></tr><tr><td>Missing data - n</td><td>4</td><td>2</td><td>2</td><td>—</td><td>—</td></tr><tr><td>CRP &gt;5 mg/l</td><td>141 (58.3)</td><td>87 (55.8)</td><td>54 (62.8)</td><td>1.34 [0.78; 2.30]</td><td>0.29</td></tr><tr><td>Elevated transaminases</td><td>77 (30.7)</td><td>53 (32.5)</td><td>24 (27.3)</td><td>0.78 [0.44; 1.38]</td><td>0.40</td></tr><tr><td>Elevated GGT</td><td>85 (33.7)</td><td>57 (34.8)</td><td>28 (31.8)</td><td>0.88 [0.50; 1.52]</td><td>0.64</td></tr><tr><td>Anemia</td><td>15 (5.9)</td><td>11 (6.7)</td><td>4 (4.5)</td><td>0.65 [0.20; 2.12]</td><td>0.49</td></tr><tr><td>Missing data - n</td><td>16</td><td>11</td><td>5</td><td>—</td><td>—</td></tr><tr><td>Zinc deficiency</td><td>79 (31.7)</td><td>44 (27.5)</td><td>35 (39.3)</td><td>1.71 [0.99; 2.96]</td><td>0.06</td></tr><tr><td>Missing data - n</td><td>4</td><td>4</td><td>0</td><td>—</td><td>—</td></tr><tr><td>Vitamin B9 deficiency</td><td>89 (35.3)</td><td>57 (35.0)</td><td>32 (36.0)</td><td>1.04 [0.61; 1.79]</td><td>0.88</td></tr><tr><td>Missing data - n</td><td>1</td><td>1</td><td>0</td><td>—</td><td>—</td></tr><tr><td>Vitamin B12 deficiency</td><td>49 (19.4)</td><td>34 (20.7)</td><td>15 (16.9)</td><td>0.78 [0.40; 1.52]</td><td>0.46</td></tr><tr><td>Vitamin D deficiency</td><td>227 (89.7)</td><td>153 (93.2)</td><td>74 (83.1)</td><td>0.35 [0.16; 0.81]</td><td>0.01</td></tr></tbody></table>

BMI, body mass index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; CVD, cardiovascular disease; FFMI, fat-free mass index; FMI, fat mass index; GGT, gamma-glutamyl transpeptidase; HDL, high density lipoprotein; LDL, low density lipoprotein; OSAS, obstructive sleep apnea syndrome; TSH, thyroid stimulating hormone. Data are presented as n (%) with n = number of patients and as mean ± standard deviation.

<caption>**Table 7**</caption>

Multivariable analysis of factors associated with altered body composition in obesity using the Fat Mass Index (FMI)/Fat-free Mass Index (FFMI) ratio.

<table><thead><tr><th>Variables</th><th>Odds ratio [95% CI]</th><th>P-value</th></tr></thead><tbody><tr><td>Female gender</td><td>3.12 [1.02; 9.50]</td><td>0.04</td></tr><tr><td>Age</td><td>1.10 [1.06; 1.14]</td><td>&lt;0.0001</td></tr><tr><td>Body mass index</td><td>1.37 [1.26; 1.50]</td><td>&lt;0.0001</td></tr><tr><td>History of cancer</td><td>5.35 [1.20; 23.84]</td><td>0.03</td></tr><tr><td>Phase angle</td><td>0.42 [0.27; 0.66]</td><td>0.0002</td></tr></tbody></table>

CI, confidence interval.

sarcopenic obesity [13] suggests using increased FM and reduced SMM/W*100, while indicating that the proposed definitions, methods, and cutoffs are subject to change according literature data. Our findings strongly support the need for future work to refine the methods of body composition measurements in the obese patients, and especially for sarcopenic obesity diagnosis.

Credit author statement

Mathilde Fichet: data collection, data analysis and interpretation, writing- original draft. Estelle Le Pabic: data analysis, review & editing. Laurence Lacaze: data acquisition. Mickaël Som: data
acquisition. **Emeric Rageul:** data acquisition. **Mathilde Barrallier:** data acquisition. **Mylene Guiho:** data acquisition. **Damien Bergeat:** data acquisition, writing - review & editing. **Ronan Thibault:** data collection, data analysis and interpretation, writing- original draft, review & editing, supervision.

## Funding statement

This study did not receive any specific fund.

## Conflict of interest

No conflicts of interest, financial or otherwise, in relation with this study are declared by the authors.

## Acknowledgements

We thank all the nurse and medical staff from the Department of Endocrinology-Diabetology-Nutrition at Rennes University Hospital, Rennes, France.

## References

[1] Prado CMM, Wells JCK, Smith SR, Stephan BCM, Siervo M. Sarcopenic obesity: a Critical appraisal of the current evidence. Clin Nutr 2012;31:583–601.

[2] Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48:16–31.

[3] Morley JE, Abbatecola AM, Argiles JM, Baracos V, Bauer J, Bhasin S, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc 2011;12:403–9.

[4] Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol: Series A 2014;69:547–58.

[5] Chen L-K, Liu L-K, Woo J, Assantachai P, Auyeung T-W, Bahyah KS, et al. Sarcopenia in asia: consensus report of the Asian working group for sarcopenia. J Am Med Dir Assoc 2014;15:95–101.

[6] Johnson Stoklossa CA, Forhan M, Padwal RS, Gonzalez MC, Prado CM. Practical considerations for body composition assessment of adults with class II/III obesity using bioelectrical impedance analysis or dual-energy X-ray absorptiometry. Curr Obes Rep 2016;5:389–96.

[7] Gusmao-Sena MH. Association between sarcopenic obesity and cardiovascular risk: where are we? Nutr Hosp 2016;33.

[8] Rolland Y, Lauwers-Cances V, Cristini C, van Kan GA, Janssen I, Morley JE, et al. Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in community-dwelling elderly women: the EPIDOS (EPI-Demiologie de l'OSteoporose) Study. Am J Clin Nutr 2009;89:1895–900.

[9] Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res 2004;12:1995–2004.

[10] Tian S, Xu Y. Association of sarcopenic obesity with the risk of all-cause mortality: a meta-analysis of prospective cohort studies: sarcopenic obesity and mortality. Geriatr Gerontol Int 2016;16:155–66.

[11] Batsis JA, Barre LK, Mackenzie TA, Pratt SI, Lopez-Jimenez F, Bartels SJ. Variation in the prevalence of sarcopenia and sarcopenic obesity in older adults associated with different research definitions: dual-energy X-ray absorptiometry data from the national health and nutrition examination Survey 1999–2004. J Am Geriatr Soc 2013;61:974–80.

[12] Donini LM. Critical appraisal of definitions and diagnostic criteria for sarcopenic obesity based on a systematic review. Clin Nutr 2020;21.

[13] Donini LM, Busetto L, Bischoff SC, Cederholm T, Ballesteros-Pomar MD, Batsis JA, et al. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Obes Facts 2022;15:321–35.

[14] Auyeung TW, Lee JSW, Leung J, Kwok T, Woo J. Adiposity to muscle ratio predicts incident physical limitation in a cohort of 3,153 older adults—an alternative measurement of sarcopenia and sarcopenic obesity. AGE 2013;35: 1377–85.

[15] de Oliveira Silva A, Karnikowski MG, de O, Schwerz Funghetto S, Prestes J, Stival L, et al. Association of body composition with sarcopenic obesity in elderly women. Int J Graph Multimed 2013;25.

[16] Xiao J, Cain A, Purcell SA, Ormsbee MJ, Contreras RJ, Kim J-S, et al. Sarcopenic obesity and health outcomes in patients seeking weight loss treatment. Clinical Nutrition ESPEN 2018;23:79–83.

[17] Biolo G, Di Girolamo FG, Breglia A, Chiuc M, Baglio V, Vinci P, et al. Inverse relationship between “a body shape index” (ABSI) and fat-free mass in women and men: insights into mechanisms of sarcopenic obesity. Clin Nutr 2015;34: 323–7.

[18] Xiao J, Purcell SA, Prado CM, Gonzalez MC. Fat mass to fat-free mass ratio reference values from NHANES III using bioelectrical impedance analysis. Clin Nutr 2018;37:2284–7.

[19] Expert Panel on Detection, Evaluation, and Treatment of High Blood Choles- terol in Adults. Executive summary of the third report of the national cholesterol education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA. J Am Med Assoc 2001;285:2486–97.

[20] Thibault R, Makhlouf AM, Mulliez A, Gonzalez MC, Kekstas G, Rotovnik Kozjek N, et al. Phase angle project Investigators. Fat-free mass at admission predicts 28-day mortality in intensive care unit patients: the international prospective observational study PHASE ANGLE PROJECT. Intensive Care Med 2016;42:1445–53.

[21] Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89: 465–71.

[22] Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. J Clin Nutr 2000;72:694–701.

[23] Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarco- penia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889–96.

[24] Rennie D, editor. Users' guides to the medical literature: a manual for evidence-based clinical practice. 2nd ed. New York: McGraw Hill Medical; 2008.

[25] Arnáiz EG, Cobo DA, Estébanez B, Barajas Galindo D, Pintor de la Maza B, Urioste Fondo A, et al. Prevalence of sarcopenic obesity according to different diagnostic methods and cut-off points in candidates for bariatric surgery. Clin Nutr 2024;43:1087–93.

[26] Kalinkovich A, Livshits G. Sarcopenic obesity or obese sarcopenia: a cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Res Rev 2017;35:200–21.

[27] Song M-Y, Ruts E, Kim J, Janumala I, Heymsfield S, Gallagher D. Sarcopenia and increased adipose tissue infiltration of muscle in elderly African American women. Am J Clin Nutr 2004;79:874–80.

[28] Waki M, Kral JG, Mazariegos M, Wang J, Pierson Jr RN, Heymsfield SB. Relative expansion of extracellular fluid in obese vs. nonobese women. Am J Physiol 1991;261(2 Pt 1):E199–203.

[29] Sternfeld B. Associations of body composition with physical performance and self- reported functional limitation in elderly men and women. Am J Epi- demiol 2002;156:110–21.

[30] Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Defi- nition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011;12:489–95.

[31] Di Vincenzo O, Marra M, Di Gregorio A, Pasanisi F, Scalfi L. Bioelectrical impedance analysis (BIA) -derived phase angle in sarcopenia: a systematic review. Clin Nutr 2021;40:3052–61.

[32] Cristancho C, Mogensen KM, Robinson MK. Malnutrition in patients with obesity: an overview perspective. Nutr Clin Pract 2024;39:1300–16.

[33] Hamer M, O'Donovan G. Sarcopenic obesity, weight loss, and mortality: the English Longitudinal Study of Ageing. Am J Clin Nutr 2017;106:125–9.

[34] Rossi AP, Bianchi L, Volpato S, Bandinelli S, Guralnik J, Zamboni M, et al. Dynapenic abdominal obesity as a predictor of worsening disability, hospi- talization, and mortality in older adults: results from the InCHIANTI study. J Gerontol: Series A 2017;72:1098–104.

[35] Silva AM, Matias CN, Nunes CL, Santos DA, Marini E, Lukaski HC, et al. Lack of agreement of in vivo raw bioimpedance measurements obtained from two single and multi-frequency bioelectrical impedance devices. Eur J Clin Nutr 2019;73:1077–104.

[36] Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 2010;29: 560–5.