Anne -Galibert

Marie-Dominique Pu-Ph

Bardou-Jacquet Edouard

Laviolle Bruno

Vincent Pu-Ph

Christophe Leclercq

Pu-Ph

Lederlin Cardiologie

Mathieu

Alain Professeur Emérite

Astrid Pu-Ph

Philippe Pu-Ph

Mahe Cardiologie

Guillaume

Yannick Professeur Emérite

Romain Mathieu-Sanquer

Urologie Pu-Ph

Eric Mener

Professeur

Christian Professeur

Emérite Maladies

Romain Pu-Ph

Xavier Pu-Ph

Anatomie Morel

Vincent Professeur

Frédéric Pu-Ph

Ophtalmologie Myhie

Didier Professeur

Sylvie Pu

Prenom Titre

Allory Emmanuel

Laurence Mcu-Ph

Alexis Arnaud

Mcu-Ph Chirurgie

Banatre Agnès

Jean Marc

Mcu-Ph Physiologie

Bertheuil Nicolas

Brochard Charlène

Cabillic Florian

Joël Castelli

Cancérologie Mcu-Ph

Aline A Corvol

Fabrice Monsieur Le Professeur

Bonnet

A Madame

Sylvie Martin

/09/2020 NOM PRENOM TITRE SOUS-SECTION CNU

Merci de m'avoir fait l'honneur de présider ce jury

Table of Contents

List of illustrations

Introduction

Malnutrition is a pathological condition caused by a decrease of food intake, an increase of metabolic expenditure, an increase of energy losses or the combination of two or three of these events.

It is characterized by a decrease in fat-free mass, due to muscle mass, as well as other changes in body composition (decrease in fat mass, increased total body water) and physiological functions [START_REF] Cederholm | ESPEN guidelines on definitions and terminology of clinical nutrition[END_REF]. The prevalence of malnutrition in hospitalized patients is estimated to be 30 to 40% in France [START_REF] Preiser | Epidémiologie de la dénutrition chez le malade hospitalisé[END_REF]. The prevalence of malnutrition in particularly high in the older people; it is estimated between 23 and 60% in acute care [START_REF] Agarwal | Malnutrition in the elderly: A narrative review[END_REF].

The diagnosis of malnutrition for adults under 70 years has been recently updated by the Global Leadership Initiative on Malnutrition (GLIM) international consensus in 2018, and in France by Haute Autorité de Santé (HAS) in 2019 [START_REF] Cederholm | GLIM criteria for the diagnosis of malnutrition -A consensus report from the global clinical nutrition community[END_REF][START_REF]Haute Autorité de santé[END_REF]. Malnutrition in this population is now diagnosed by the presence of at least one phenotypic criterion and at least one etiologic criterion. Phenotypic criteria are a significant weight loss, low body mass index (BMI), and a quantified reduction in muscle mass and / or function. Reduction in muscle function can be defined by a low handgrip strength measured by a dynamometer [START_REF]Haute Autorité de santé[END_REF]. Reduction in muscle mass can be defined by a low muscle mass index or a low Fat-Free Mass Index (FFMI), measured by bioimpedance analysis (BIA) [START_REF]Haute Autorité de santé[END_REF]. Etiologic criteria are a reduction in food intake, any chronic gastro-intestinal condition that adversely affects food assimilation or absorption, or an acute or chronic disease [START_REF]Haute Autorité de santé[END_REF]. In the older population, the updated recommendations for the diagnostic criteria for malnutrition are expected for 2022. Awaiting the update, malnutrition should be diagnosed with the criteria published by HAS in 2007 [START_REF]Recommandations professionnelles : Stratégie de prise en charge en cas de dénutrition protéino-énergétique chez la personne âgée 2007:25[END_REF]. The diagnostic of malnutrition adults over 70 years requires on the presence of one or more of the following criteria: significant weight loss, BMI under 21 kg/m², albuminemia under 35 g/L or Mini Nutritional Assessment (MNA) under 17 [START_REF]Recommandations professionnelles : Stratégie de prise en charge en cas de dénutrition protéino-énergétique chez la personne âgée 2007:25[END_REF].

The diagnosis of malnutrition is a necessity regarding its complications. Malnutrition negatively impacts the wound healing process [START_REF] Stechmiller | Understanding the Role of Nutrition and Wound Healing[END_REF] by prolonging the inflammation phase, decreasing fibroblast proliferation and altering collagen synthesis [START_REF] Campos | Assessment and nutritional aspects of wound healing[END_REF]. A perfect example is the occurrence of pressure ulcers, In a cross-sectional study of hospitalized and nursing home patients, Shahin et al. [START_REF] Shahin | The relationship between malnutrition parameters and pressure ulcers in hospitals and nursing homes[END_REF] have shown that pressure ulcers were related to weight loss, low nutritional intake and low BMI. In the same way, malnutrition is also associated with increase in nosocomial infections. Schneider et al. [START_REF] Schneider | Malnutrition is an independent factor associated with nosocomial infections[END_REF] have shown that the severity of weight loss and hypoalbuminemia at admission are associated with an increased risk of nosocomial infections during the hospital stay. Main infection sites were urinary tract and surgical site. More infections induce an increase use of antibiotics as it has been shown by Pressoir et al. in a prospective multicenter study of 1545 patients [START_REF] Pressoir | Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres[END_REF]. Surgery in malnourished patients leads to more post-operative complications: bleeding, infections, wound dehiscence, circulatory insufficiency, renal failure, hepatic dysfunction, etc. [START_REF] Bozzetti | Postoperative complications in gastrointestinal cancer patients: The joint role of the nutritional status and the nutritional support[END_REF] Malnutrition is significantly associated with high fall risk in hospitalized older people, as demonstrated by Aldy et al. [START_REF] Adly | Relationship between malnutrition and different fall risk assessment tools in a geriatric in-patient unit[END_REF] in a cross-sectional study. Moreover, Charlton et al. [START_REF] Charlton | Poor nutritional status of older subacute patients predicts clinical outcomes and mortality at 18 months of followup[END_REF] have shown that older patients, that are admitted to a subacute setting, in a malnourished state, experience poorer clinical outcomes (admission to higher level care and/or dead during hospitalization), and an increased risk of mortality within the subsequent 18 months, compared with their well-nourished peers.

Increased mortality and length of stay related to malnutrition are also found in the community setting [START_REF] Hernández Mpv M León-Sanz | on behalf of the PREDyCES® researches[END_REF][START_REF] Guest | Health economic impact of managing patients following a community-based diagnosis of malnutrition in the UK[END_REF]. In an observational study performed in 1996 patients from The Health Improvement Network (THIN) database, Guest et al. [START_REF] Guest | Health economic impact of managing patients following a community-based diagnosis of malnutrition in the UK[END_REF] have shown that malnourished patients utilized significantly more healthcare resources: they had more general practitioner consultations than non-malnourished patients, three times more hospital admissions, and the length of hospital stay was significantly longer.

In 2021, in the PREDyCES study leads by Álvarez-Hernández et al. [START_REF] Hernández Mpv M León-Sanz | on behalf of the PREDyCES® researches[END_REF], malnutrition was associated with an increase in length of hospital stay, with an associated additional cost of 1,409 € per malnourished patients compared to the non-malnourished. The difference was even greater between patients not presenting with malnutrition at any time and those who were non-malnourished at admission but were malnourished at discharge: the additional cost was 5,829 € per patients.

The early identification of malnutrition is of utmost importance since a dedicated management could improve the clinical outcomes. Indeed, Schuetz et al. [START_REF] Schuetz | Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial[END_REF] have conducted a large multi-center study, including 2088 patients at risk of malnutrition. They showed that early screening then management of malnutrition improved important clinical outcomes as mortality, admission to the intensive unit care, readmission after discharge and decline of in functional status.

Altogether, these data indicate the importance of an early screening and diagnosis of malnutrition in the hospital setting to undergo the early as possible the appropriate nutritional care. However, since the publication of the GLIM and HAS criteria, little is known about the GLIM or HAS phenotypic profile of malnourished hospitalized patients, and how they are managed in the daily practice of a Nutrition Support Team (NST) in a University Hospital. There are also only a few data about the clinical outcomes (mortality, infections and 1-month hospital readmission) of the malnourished patients managed by the NST.

In 2019, one out of 11 adults had diabetes in the world [START_REF]IDF Diabetes Atlas[END_REF]. In France in 2015, 3.7 million people were taking an anti-diabetic treatment in France, i.e., 5.4% of the population had diabetes, of any type [START_REF] Mandereau-Bruno | Prévalence du diabète traité pharmacologiquement (tous types) en France en 2015. Dysparités territoriales et socio-économiques[END_REF]. Prevalence of malnutrition in older diabetic hospitalized patients has been estimate at 21,2% by Sanz-Paris et al. [START_REF] Sanz | PREVALENCIA DE DESNUTRICIÓN EN ANCIANOS HOSPITALIZADOS CON DIABETES[END_REF] a. and 18,5% by Liu et al. [START_REF] Liu | Pilot study of the Mini Nutritional Assessment on predicting outcomes in older adults with type 2 diabetes[END_REF]. Type 2 diabetes mellitus is closely linked to malnutrition as it has been shown that leads to a loss of skeletal muscle. Park et al. [START_REF] Park | Excessive Loss of Skeletal Muscle Mass in Older Adults With Type 2 Diabetes[END_REF] have examined the body composition of 2675 older adults aged 70-79 years, with dual energy X-ray absorptiometry (DXA), annually for 6 years and measured their mid-thigh muscle cross-sectional area with computed tomography at year 1 and year 6. Fifteen per cent of the patients had a known diabetes and with a systematic 75-g oral glucose tolerance test, they newly diagnosed diabetes in 8.4% of the patients. The patients who report the highest prevalence of body weight loss were older people with undiagnosed diabetes, followed by those with previously known diabetes, and then those without diabetes, especially in women. The annual declines in appendicular lean mass were higher in older adults with previously unknown diabetes and known diabetes than in those without diabetes. Sugimoto et al. [START_REF] Sugimoto | Hyperglycemia in nonobese patients with type 2 diabetes is associated with low muscle mass: The Multicenter Study for Clarifying Evidence for Sarcopenia in Patients with Diabetes Mellitus[END_REF] have shown in the MUSCLES-DM study that loss of muscle mass was directly related to hyperglycemia. This study includes 768 Japanese patients with stable type 2 diabetes aged over 40 years. They found that glycated hemoglobin (HbA1c) level was specifically associated with low skeletal muscle mass index measured by BIA. These data suggest that diabetic patients could be at high risk of severe malnutrition. Yet, the phenotype of malnourished diabetic patients based on the GLIM or HAS criteria has never been studied.

The primary objective of this study was to determine whether diabetes mellitus is a risk factor for a severe form of hospital malnutrition based of the newly published HAS criteria.

The secondary objectives were to:

-Compare the levels of fat-free mass, muscle strength, and food intake between diabetic and non-diabetic patients; -Characterize the profile of malnourished patients or patients at risk of malnutrition managed by the NST; -Study the prevalence of loss of fat-free mass, in patients with anthropometric phenotypic criteria of malnutrition, i.e. weight loss or low BMI; -Identify the factors associated with mortality in our population of malnourished patients or patients at risk of malnutrition, and especially to assess whether the HAS thresholds of fat-free mass or muscle strength were associated with patient mortality.

Material and Methods

Study design and population

We conducted an observational, retrospective, cross-sectional, monocentric study at CHU Rennes from November 2019 to May 2021 in a population of patients hospitalized and followed by the Nutrition Support Team (NST) (Unité Transversale de Nutrition). The CHU Rennes NST is composed of a resident (working 70% of time, 30% of the remaining time being devoted to the Diabetes Support Team), supervised on a daily basis by a senior physician, and a dietician (working 40% of time). The NST is responsible for the care of malnourished or at risk for malnutrition hospitalized patients. NST health caregivers offer assistance in screening for malnutrition, particularly in targeted departments: digestive surgery, ENT and urology. Twice a week, a systematic screening of malnutrition is performed. A computer program analyzes the data from the medical record (DxCare) of the patients hospitalized in these three departments. Patients at risk of malnutrition are identified according to one of the following criteria:

-BMI lower than 21 kg/m²; -BMI higher than 30 kg/m² (obese people are at risk for sarcopenic obesity); -Decreased food intake assessed by the food monitoring sheet filled in by the paramedical staff or a low Simple Evaluation of Food Intake (SEFI)® score (<7) [24]; -Albuminemia under 30 g/L. Thus, about six patients per week are identified as at risk of malnutrition. Then, the dietician and the resident interviewed and examined these patients to confirm the diagnostic of malnutrition and to advice about their nutritional management. NST health caregivers are also giving their advice for the prescription of nutrition support on the request of physicians from all the hospital departments.

For this study, we included all the patients seen by the NST from November 2019 to May 2021 and for whom a bioimpedance analysis (BIA) had been performed. If BIA was not performed, the patient was excluded. Note that during the Covid-19 epidemic, the NST consultation activity in hospital wards was replaced by phone consultations during several weeks, explaining a lower number of included patients than expected (n=540).

Definitions

We defined malnutrition according to the 2019 [START_REF]Haute Autorité de santé[END_REF] and 2007 (for older people) [START_REF]Recommandations professionnelles : Stratégie de prise en charge en cas de dénutrition protéino-énergétique chez la personne âgée 2007:25[END_REF] HAS criteria: presence of at least one phenotypic criterion and one etiologic criterion.

Phenotypic criteria are:

-A weight loss of at least 5% in 1 month or at least 10% in 6 months or at least 10% compared to the usual weight before the beginning of the disease; -A BMI under 18.5 kg/m² for patients under 70 years, a BMI under 21 kg/m² for older patients; -A quantified reduction in muscle mass and/or function:

 Grip strength under 26 kg for men and under 16 kg for women  FFMI under 17 kg/m² for men and under 15 kg/m² for women To diagnose malnutrition in the older subjects, we voluntarily did not use the criteria of serum albumin and MNA as we supposed that they will not be used as they are in the HAS recommendation 2022 update.

Etiologic criteria are:

-A reduction in food intake, assessed by the SEFI® < 7, to match with the HAS criteria of at least 50% for more than 1 week, or any reduction in intake for more than 2 weeks relative to quantified usual food intake, or estimated protein-energy requirements; -Reduced absorption (malabsorption/maldigestion) -A situation of aggression (protein hypercatabolism with or without inflammatory syndrome): acute pathology or evolving chronic pathology or evolving malignant pathology.

Then, according to HAS criteria, patients were classified according to malnutrition severity.

Moderate malnutrition was defined by one of the following criteria:

-A BMI between 17 and 18,5 kg/m² for patients under 70 years and a BMI between 18 and 21 kg/m² for older patients.

-A loss of weight > 5% in 1 month or > 10% in 6 months or > 10% compared to the usual weight before the onset of the disease Severe malnutrition was defined by one of the following criteria:

-A BMI under 17 kg/m² for patients under 70 years and a BMI under 18.5 for older patients.

-A loss of weight > 10% in 1 month or > 10 % in 6 months or > 10% compared to the usual weight before the onset of the disease -Serum albumin level less than 30 g/L.

Assessment of body composition by bioimpedance analysis (BIA)

Body composition was assessed by BIA. BIA was performed by the NST resident at patient's bed during the first consultation. The device used was a double frequency bioimpedancemeter (5 kHz and 50 kHz) (Bodystat 1500 MDD, Bodystat Ltd, Man Island, UK) (Figure 1). The patient had to be lying down for at least five minutes, wearing underwear. Four single-use electrodes were placed on the patient's right or left hemisphere. Two electrodes were placed on the upper limb: one on the back of the hand and the other on the back of the wrist. Two electrodes were placed on the lower limb: one on the dorsal surface of the foot and the other on the anterior surface of the ankle (Figure 1). Dedicated cables connected the device to the appropriate electrodes. The patient was asked to touch anything, and particularly to not touch the cables, the impedancemeter, or any metal object. The operator entered in the device software the patient's gender, age, weight and height in order to start the measurement that took about five seconds. The whole procedure took about 5 minutes. Through a safe battery generated signal, the resistance R (in Ohm) which depends on the intraand extra-cellular water and the reactance X which depends on the cell members, were measured. These parameters were used to automatically calculate the phase angle α (in degrees) and the impedance Z (in Ohm) according to the formula Z² = R² + X². Then, from multi-variable regression equations preregistered (and kept secret by the manufacturer) in the bioimpedancemeter using sex, age, weight and height, several parameters are obtained:

-Fat mass (in kg and % of total body mass), -Fat-free mass (in kg and % of total body mass), -Dry fat-free mass (in kg), -Total body water (in L and % of total body mass), -Extracellular water (in L and % of total body mass), -Intracellular water (in L and % of total body mass).

The Body Fat Mass Index (BFMI = body fat mass (kg) / height (m)²) and the Fat-free Mass Index (FFMI = fat-free mass (kg) / height (m)²) were then calculated. FFMI, was considered low, i.e. in favor of malnutrition, if < 15 in females or < 17 in males.

Assessment of food intake

Food intake was assessed using the SEFI®[24] (Figure 2), which is a simple tool that can be used in two ways:

-A visual analogue scale: the patient indicates the amount of food she/he presently eats using the slider on the scale between "nothing at all" to "as usual". The result, between 1 and 10, is seeing on the verso of the scale.

-A visual assessment of consumed portions: the patient assesses the portions she/he eats during her/his last meal, generally lunch. The interpretation of the results of the SEFI® is based on the results clinical studies. In adult patients malnourished or at risk of malnutrition, a strong correlation has been shown between the score of the visual analogue scale and the daily energy intakes assessed by the 3-day dietary record [START_REF] Thibault | Use of 10-point analogue scales to estimate dietary intake: a prospective study in patients nutritionally at-risk[END_REF]. In this study, 81% of those with scores < 7 were at risk of malnutrition according to the Nutritional Risk Index (NRI). These results were confirmed by an independent study conducted in 1762 medical oncology patients [START_REF] Guerdoux-Ninot | Assessing dietary intake in accordance with guidelines: Useful correlations with an ingesta-Verbal/Visual Analogue Scale in medical oncology patients[END_REF].

Assessment of handgrip strength

Handgrip strength was assessed using an electronic handheld dynamometer (Camry EH101 digital hand dynamometer -90 kg / 200 lbs., Camry Scale Store, California, USA) (Figure 3).

Figure 3-Handgrip dynamometer

The patient had to be sitting. The patient had to grip the dynamometer as hard as possible, during 5 seconds, using her/his dominant hand. The arm had to be bend at 90°, without pressing the instrument against the body or helping with the other hand. Three tests were performed and the best performed was used for the analysis. Handgrip strength was considered low, i.e. in favor of malnutrition, if < 16 kg in females and < 26 kg in males. Comorbidities or history considered were cardiovascular, respiratory, autoimmune, gastrointestinal (GI), psychiatric, oncological and urorenal comorbidities, active or passive misuse of tobacco, alcohol or other toxic substances. Patients were also classified according to whether their comorbidities were associated with malabsorption/maldigestion or not and whether they were receiving or had recently received chemotherapy (less than 6 months). Serum albumin (g/L) and HbA1c (%) data were obtained from the routine blood tests performed at CHU Rennes during the hospital stay and were collected from the computerized medical record.

Data collection

The second set of data were collected retrospectively, one month after patient's discharge:

-Length of hospital stay (day); -Possible complications of malnutrition: pressure sores, infection, surgical complication, refeeding syndrome; -Hospital readmission at CHU Rennes one month after the release.

For diabetic patients, were collected the type of diabetes, its duration, treatment and microvascular and/or macrovascular complications, as well as HbA1c level to estimate the glycemic control.

Statistics

For data description, quantitative results are reported as mean ± standard deviation (min; Q1; median; Q3; max) and qualitative results are expressed as numbers (%). For group comparison, Student test, or Mann-Whitney test if necessary, were used to compare quantitative variables. The Chi2 test, or Fisher test if necessary, were used to compare quantitative variables. Factors associated with death and hospital readmission were identified using stepwise logistic regressions and presented by Odds Ratio (OR) and its 95% Confidence Interval (CI). Variables significant at 0.20 were included in a multivariate model and a stepwise top-down selection was performed. All statistical tests had a significance level of 0.05. Statistical analyses were performed using SAS software, v.9.4® (SAS Institute, Cary, NC, USA).

Results

Description of the study population

In our study, 231 patients were included. The mean age was 62.2±15.9 years. Half of them were hospitalized in the digestive surgery department (50.2%). The others were in other surgery departments (16.5%), in medicine departments (30.3%) or in intensive care units (3.0%). Around 50% (48.1%) of our patients had a history of cancer (Table 1). The mean BMI at admission was 23.5±6.4 kg/m². Seventy-five % of patients reported a low SEFI® (< 7), 54.4% had a low FFMI, and 49.7%, a low handgrip strength.

Description of the diabetic population

Forty-seven patients (20.3%) had diabetes, including 68.1% of type 2 diabetes (Table 1). There were more males in the diabetic subgroup. This population had more cardiovascular and GI diseases, including more liver transplantation. This may explain why they were more presented with ascites than the non-diabetic patients were. Diabetes was unbalanced for 48.9% of them and 30.4% had at least one complication (Table 2).

The weight of diabetic patients was more missing at admission than for the non-diabetic patients (89.1% vs 74.5%, p=0.0094). The BMI of diabetic patients was higher than in non-diabetic patients weight in the last month, in the last 6 months, or compared to their usual weight (Table 3). Oral nutritional supplements and enteral nutrition were more often provided. During the follow-up, the occurrence of a complication, death, or hospital readmission one month after hospital release were not significantly different from the non-malnourished population.

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Description of the phenotypic criteria of the malnourished patients according to the HAS malnutrition diagnostic criteria

All the included hospitalized patients had, by definition, an etiological criterion of malnutrition: situation of metabolic stress with protein hypercatabolism, due to an acute disease. To be considered as malnourished, the patients should report one or more phenotypic criteria: low BMI for 29% of them, weight loss compared to the usual weight in 62%, low FFMI in 64.4%, and low handgrip strength in 58.6% (Table 4). The malnutrition diagnosis was based on one phenotypic criterion for 36.8% patients, two criteria for 33.7%, three criteria for 19.2%, and four criteria for 10.4% (Table 4).

Factors associated with mortality

In our study, the mortality rate was 11.3%. Univariate analysis showed that patients hospitalized in medical or non-digestive surgical departments had a greater risk of dying during hospitalization or in the months following hospitalization. Patients hospitalized for cancer management (especially nondigestive cancers) were at greater risk of mortality. Clinical amyotrophy was significantly associated with mortality. Malnutrition, severity of malnutrition, BMI, low FFMI and low handgrip strength were not significantly associated with mortality. Cardiovascular or respiratory associated diseases, admission department, weight loss in 6 months, amyotrophy, SEFI® score or SEFI® in classes, 50 kHz phase angle, low handgrip strength, and diabetes, were included in the stepwise top-down multivariate logistic model as they were associated with mortality (P<0.20) in the univariate analysis. The multivariate analysis did not show any factor significantly associated with mortality. The last remaining variable was diabetes (p=0.0604), but it was below the level of statistical significance (Table 5). To our knowledge, our study is the first to characterize the phenotype of malnourished diabetic patients based on the newly published HAS criteria. We showed that diabetes mellitus was associated with lower handgrip strength but higher fat-free mass. Diabetic patients had poorer outcomes, especially mortality, unless it fell to reach the statistical level of significance in the multivariate analysis. None of the diagnostic criteria for malnutrition was associated with mortality. Diabetes mellitus was not associated with a higher risk of severe malnutrition. Finally, our data constitute an evaluation of the CHU Rennes NST practices that would help us to improve our practice in the future. We report that 64.4

% of malnourished patients had a low FFMI justifying the need of a global assessment of malnutrition as proposed by the GLIM and the HAS.

Diabetes and sarcopenia

In the literature, more articles are found regarding the relation between diabetes and sarcopenia [START_REF] Kim | Prevalence and Determinant Factors of Sarcopenia in Patients With Type 2 Diabetes: The Korean Sarcopenic Obesity Study (KSOS)[END_REF][START_REF] Nguyen | Assessment of nutritional status in older diabetic outpatients and related factors in Hanoi, Vietnam[END_REF][START_REF] Anagnostis | Type 2 Diabetes Mellitus is Associated with Increased Risk of Sarcopenia: A Systematic Review and Meta-analysis[END_REF][START_REF] Mesinovic | Sarcopenia and type 2 diabetes mellitus: a bidirectional relationship[END_REF][START_REF] Cleasby | Insulin resistance and sarcopenia: mechanistic links between common co-morbidities[END_REF] than with malnutrition. Sarcopenia is a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength. It results in a decrease in physical performance. It is associated with a risk of falls, fracture, physical disability, decrease of quality of life, and death [START_REF] Bunout | Association between sarcopenia and mortality in healthy older people: Sarcopenia and mortality[END_REF]. According to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), sarcopenia can be classified in two categories: i) primary sarcopenia that is age-related; ii) secondary sarcopenia that occurs in the context of systemic diseases, especially those involving inflammatory processes, malignancy or organ failure. Physical inactivity and low protein intake contribute to sarcopenia [START_REF] Bahat | Sarcopenia: revised European consensus on definition and diagnosis[END_REF]. In our study we did not assess the prevalence of sarcopenia based on the EWGSOP2 criteria, but we used the malnutrition diagnosis criteria recently published by the GLIM and the HAS. These latter criteria propose to use separately of the other, the assessment of fat-free mass or muscle mass, and the muscle strength.

In 2020, a meta-analysis reviews and synthesizes the available evidence regarding the association between diabetes mellitus and sarcopenia [START_REF] Anagnostis | Type 2 Diabetes Mellitus is Associated with Increased Risk of Sarcopenia: A Systematic Review and Meta-analysis[END_REF]. The authors showed that the risk of sarcopenia was significantly increased in type 2 diabetic patients compared with euglycemic subjects (n=15 studies, OR 1.55, 95% CI 1.25-1.91, p<0.001; I² 34.6%). The diabetic patients presented lower values of handgrip strength (n=6 studies (standardized mean difference (SMD) -0.24, 95% CI -0.39 to -0.09, p=0.002)), but no difference in skeletal mass index was observed (SMD 0.018, 95% CI 0.047 to -0.075, p=0.11; I² 25.6%), which is consistent with our study. However, a large study including 746 diabetic non-obese patients and 2067 controls showed that the frequency of sarcopenia increased linearly with the HbA1c level. HbA1c level was specifically associated with low skeletal mass index [START_REF] Sugimoto | Hyperglycemia in nonobese patients with type 2 diabetes is associated with low muscle mass: The Multicenter Study for Clarifying Evidence for Sarcopenia in Patients with Diabetes Mellitus[END_REF].

explaining a higher FFMI in the diabetic patients of our study. In addition, we could not completely exclude that the presence of edema could have made overestimating fat-free mass in our hospitalized patients.

Loss of muscle strength

The measurement of handgrip strength by dynamometer is another easy method to diagnose malnutrition that could be performed at bedside after a short training. In our study, handgrip strength had a very good negative predictive value to predict survival. Some studies suggested that handgrip strength could have a better performance than the measurement of muscle mass to assess outcome. For example, a meta-analysis conducted in 2012 and including seven articles showed an overall association between low muscle strength and functional decline (pooled OR=1.86, 95% CI: 1.32, 2.64; I²=91%), whereas low muscle mass was not [START_REF] Schaap | Adiposity, Muscle Mass, and Muscle Strength in Relation to Functional Decline in Older Persons[END_REF]. ABC study [START_REF] Park | Decreased Muscle Strength and Quality in Older Adults With Type 2 Diabetes: The Health, Aging, and Body Composition Study[END_REF]. They evaluated handgrip and knee extensor strength in 3075 well-functioning older adults of whom 80.2% were diabetics. In men, diabetics showed significantly lower muscle strength in both upper and lower extremities although muscle mass was significantly greater, assessing by DXA.

No difference in muscle strength was found in women, despite their muscle mass was also higher. In a younger population, aged 30 to 79 years with a mean age of 50,7 years, Bo Kyung Koo [START_REF] Koo | The Differential Association between Muscle Strength and Diabetes Mellitus According to the Presence or Absence of Obesity[END_REF] has also studied the muscle strength of diabetic patients. [START_REF] Andersen | Association of muscle strength and electrophysiological measures of reinnervation in diabetic neuropathy[END_REF] that showed that loss of muscle strength in diabetic patients is due to incomplete reinnervation following axonal loss. However, this finding does not explain our results as only a few diabetic patients had peripheral neuropathy. Another explanation might be the inflammation associated with diabetes mellitus. Poor glycemic control is associated with increased systemic inflammatory cytokines, such as tumor necrosis factor-alpha [START_REF] Qiao | The change of serum tumor necrosis factor alpha in patients with type 1 diabetes mellitus: A systematic review and meta-analysis[END_REF] and interleukin-6 [START_REF] Bowker | Meta-analysis investigating the role of interleukin-6 mediated inflammation in type 2 diabetes[END_REF]. High level of these cytokines are reported to be associated with lower muscle mass and lower muscle strength [START_REF] Visser | Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study[END_REF].

Food intake, malnutrition, diabetes and mortality

Decrease of food intake is a good indicator of malnutrition and is known to be a risk factor for mortality. A large cross-sectional study, the NutritionDay® survey, has audited the food intake of 16'455 patients treated in 748 wards from 256 hospitals in 25 countries. The authors showed that the incidence of death increased from less than 1% for those eating all their meals to nearly 9% for those eating nothing despite being allowed to eat (p < 0.001). The mortality increased with decreased of food intake and was dramatically high in older patients [START_REF] Hiesmayr | Decreased food intake is a risk factor for mortality in hospitalised patients: The NutritionDay survey 2006[END_REF]. By improving the presentation meal of patients hospitalized, Navarro et al. [START_REF] Navarro | Improved meal presentation increases food intake and decreases readmission rate in hospitalized patients[END_REF] In diabetic population, food intake is also associated with mortality. Omura et al. [START_REF] Omura | Assessing the association between optimal energy intake and all-cause mortality in older patients with diabetes mellitus using the Japanese Elderly Diabetes Intervention Trial[END_REF] evaluated data from 756 older patients with diabetes mellitus and the association between baseline food intake and mortality. Food intake was assessed using a Yoshimura's food frequency questionnaire based on food groups. They observed a U-shaped relationship between energy intake adjusted on body weight and mortality during a 6-year follow-up period: the patients who eat less are at increased risk for mortality.

This study shows the importance of an optimal food intake in diabetic patients, particularly in the older patients already fragile and at risk of poor outcomes.

The assessment of food intake is a key part of nutritional assessment, corresponding to an etiological criterion of the GLIM and HAS criteria of malnutrition [START_REF] Cederholm | GLIM criteria for the diagnosis of malnutrition -A consensus report from the global clinical nutrition community[END_REF]. We chose to evaluate food intake with the SEFI®, since it is easy to use and offers a standardized measure of malnutrition risk based on food intake. It has shown in general practice primary care population an excellent negative predictive value (99%), a 76% sensitivity and 87% specificity for the diagnosis of malnutrition [START_REF] Bouëtté | A visual analogue scale for food intake as a screening test for malnutrition in the primary care setting: Prospective non-interventional study[END_REF]. Thibault et al. [START_REF] Thibault | Assessment of food intake in hospitalised patients: A 10-year comparative study of a prospective hospital survey[END_REF] have shown that this analogue scale is also well correlated with energy intake (p=0.82, P<0.0001) in hospitalized population, with an excellent feasibility in 114 malnourished patients or at risk of malnutrition. Accuracy for assessing malnutrition was calculated over 98 malnourished patients or at risk of malnutrition. They showed good specificity and positive predictive value (81% and 86% respectively); whereas sensitivity and negative predictive value were lower (57% and 46% respectively).

These results were confirmed by Guerdoux-Ninot et al. [START_REF] Guerdoux-Ninot | Assessing dietary intake in accordance with guidelines: Useful correlations with an ingesta-Verbal/Visual Analogue Scale in medical oncology patients[END_REF] in 1762 medical oncology hospitalized patients. They showed that patients with a SEFI® score <7 had 12-fold higher probability of being at nutritional risk (OR 12.3; 95% CI (8.7-17.4); p < .001) than those with a score ≥7. Our study is the first to evaluate the accuracy of SEFI® to evaluate the risk of mortality in a subgroup of malnourished or at risk for malnutrition patients. We did not show that SEFI ® <7 was a good predictor of mortality risk, probably because our study includes a majority of severe malnourished patients. We should reevaluate whether another cut-off of SEFI® could predict the risk for mortality.

Diabetes, infections and mortality

In our study, abdominal infection and bacteremia were more often detected in diabetic than in non-diabetic patients. The association between diabetes and infection is well known. Old in vitro studies have shown that the increase of infection is related to neutrophil dysfunction: deficiency in the chemotaxis of polynuclear leukocyte [START_REF] Mowat | Chemotaxis of Polymorphonuclear Leukocytes from Patients with Diabetes Mellitus[END_REF], reduction in phagocytic property [START_REF] Alexiewicz | Polymorphonuclear Leukocytes in Non-Insulin-dependent Diabetes Mellitus: Abnormalities in Metabolism and Function[END_REF] and defective bactericidal capability [START_REF] Naghibi | The effect of diabetes mellitus on chemotactic and bactericidal activity of human polymorphonuclear leukocytes[END_REF]. The first study that have explored the risk of developing an infection in a diabetic population was a performed in Canada in 1999. However, even if the risk for intestinal infectious disease was higher in the diabetic population, there was no association with glycemic control. The latter is consistent with our results that our diabetic patients had a good glycemic control, with mean HbA1c of 6.4±1.9 %, with an increased risk for GI infection.

Although not statistically significant, the increased risk for infections reported in the diabetic patients of our study may partly explain the association between diabetes and mortality. Diabetes mellitus is a well-known factor of all-cause hospital mortality [START_REF] Avci | Relationship Between Morbidity and Mortality and HbA1c Levels in Diabetic Patients Undergoing Major Surgery[END_REF][START_REF] Anyanwagu | Relationship between HbA1c and all-cause mortality in older patients with insulin-treated type 2 diabetes: results of a large UK Cohort Study[END_REF][START_REF] Ida | Relationship between frailty and mortality, hospitalization, and cardiovascular diseases in diabetes: a systematic review and meta-analysis[END_REF][START_REF] Wu | Predictors of mortality in hospitalized diabetic patients: A 7-year prospective study[END_REF]. Wu et al. [START_REF] Wu | Predictors of mortality in hospitalized diabetic patients: A 7-year prospective study[END_REF] conducted in 2007 an observational study including 1792 hospitalized diabetic patients, with the aim to identify the predictive factors of mortality. These factors were an age greater than 65 years, a duration of hypertension more than 5 years, 24 h proteinuria greater than 0.3 g, and an estimated creatinine clearance less than 60 mL/min. Age apart, we did not investigate any of these latter factors in our analyses. Truly, the presence of diabetes complications may explain the higher mortality risk of diabetic patients.

Practice analysis

Our data constitute an evaluation of the CHU Rennes NST practices. We have shown the importance of doing a systematic multimodal assessment of the nutritional status of hospitalized patients with the use of BIA, handgrip dynamometer and SEFI®, in addition to weight measurement at admission and during the hospitalization, in accordance with the GLIM and HAS recommendations. These tools are easy to use and the measurements are quick to perform. They should be part of the clinical examination of all hospitalized patients at their admission, in order to detect malnutrition as early as possible. To achieve this goal, the education and the training of the health caregivers are highly warranted. Moreover, with the creation of the new 'DES Endocrinology-Diabetology-Nutrition (EDN)', the EDN residents have become the cornerstone of the management of malnutrition in diabetic patients.

The screening of malnutrition in this population should be as important as the screening of diabetes complications in every patient assessment. Furthermore, the EDN resident is the best placed to manage the complications of nutritional support. Indeed, enteral nutrition and even more parenteral nutrition are associated with increased glycaemia, and the EDN resident is the one who knows how to manage the oral anti-diabetic drugs and insulin when artificial nutrition is introduced.

Study limitations

We suffer from a selection bias, as our study patients were mostly malnourished, with a large prevalence of severe malnutrition. It could explain why we could not show that diabetes was a risk factor for severe malnutrition. We could also speculate that the number of diabetic patients included was not enough to have sufficient power to found significant associations, especially with complications, mortality and 1-month hospital readmission. Particularly, the diabetic patients included in our study had few complications and half of them had a well-controlled diabetes.

Conclusion

To our knowledge, our study is the first to characterize the phenotype of malnourished diabetic patients based on the newly published HAS criteria. Twenty per cent of the patients followed by the NST are diabetic. Diabetes mellitus was not associated with a higher risk of severe malnutrition.

Diabetic patients had poorer outcomes, especially mortality, unless it fell to reach the statistical level of significance in the multivariate analysis. The diagnosis and management of malnutrition in the clinical practice at hospital is essential to prevent complications, mortality and to reduce hospital length of stay.

Diabetic patients are more susceptible to malnutrition, especially if they are older people. As they are more vulnerable, future studies should assess whether malnutrition should be evaluated with specific thresholds of fat-free mass or muscle strength in diabetic patients. The malnutrition in diabetic patients is a transversal topic that should bring together the endocrinologist and the nutritionist communities, as they are now in the newly created 'DES Endocrinology-Diabetology-Nutrition' diploma.

  List of illustrations .................................................................................................................................. Introduction ............................................................................................................................................. Material and Methods ............................................................................................................................ 1. Study design and population ....................................................................................................... 2. Definitions ................................................................................................................................... 3. Assessment of body composition by bioimpedance analysis (BIA) ........................................... 4. Assessment of food intake .......................................................................................................... 5. Assessment of handgrip strength ................................................................................................ 6. Data collection ............................................................................................................................ 7. Statistics ...................................................................................................................................... Results ................................................................................................................................................... 1. Description of the study population ............................................................................................ 2. Description of the diabetic population ........................................................................................ 3. Description of the malnourished patients .................................................................................... 4. Description of the phenotypic criteria of the malnourished patients according to the HAS malnutrition diagnostic criteria ................................................................................................... 5. Factors associated with mortality ................................................................................................ Discussion ............................................................................................................................................. 1. Summary of the results ............................................................................................................... 2. Diabetes and sarcopenia .............................................................................................................. 3. Loss of skeletal muscle mass ...................................................................................................... 4. Loss of muscle strength ............................................................................................................... 5. Food intake, malnutrition, diabetes and mortality ...................................................................... 6. Diabetes, infections and mortality............................................................................................... 7. Practice analysis .......................................................................................................................... 8. Study limitations ......................................................................................................................... Conclusion ............................................................................................................................................. References ............................................................................................................................................. Glossary .................................................................................................................................................

Table 1 -Table 2 -Table 3 -Table 4 -Table 5 -

 12345 Description of the study population. ....................................................................................... Description of diabetic population. ........................................................................................ Description of the malnourished population. ......................................................................... Phenotypic criteria and diagnostic of malnutrition. ............................................................... Univariate and multivariate analyses of factors associated with mortality. ............................

Figure 1 -

 1 Figure 1 -Bioimpedance analysis ........................................................................................................

Figure 2 -

 2 Figure 2-The Simple Evaluation of Food Intake (SEFI)® ..................................................................

Figure 3 -

 3 Figure 3-Handgrip dynamometer ........................................................................................................

Figure 1 -

 1 Figure 1 -Bioimpedance analysis.

Figure 2 -

 2 Figure 2-The Simple Evaluation of Food Intake (SEFI®)

A

  first set of data were collected prospectively, during the first consultation by the NST, and were those used for the routine use for malnutrition diagnosis and patient management: -Cause of hospitalization and history of illness; -Demographic data: age, gender, and place of residence; -Comorbidities and surgical, medical and psychiatric history; -Anthropometric data: weight, height and BMI (ratio of weight (in kg) to height squared (in m²)) on the day of the consultation; -Weight kinetics: weight 1 month before, weight 6 months before, and usual weight. These weights were obtained from the patient interview and the computerized medical record. Weight loss was considered if the patient has lost more than 1% of the initial weight; -Clinical data: presence of edema, ascites, amyotrophy or diarrhea; -Assessment of food intake by the SEFI® and considered low if under 7; -Possible intake of Oral Nutritional Supplements (ONS) or artificial nutrition (enteral and/or parenteral nutrition); -BIA to study body composition including the calculation of the FFMI, considered low if < 15 kg for females and < 17 kg for males; -Handgrip strength via a dynamometer, considered low if < 16 kg for females and < 26 kg for males.

Table 1 -

 1 Description of the study population.

	Variables	Global population (n = 231)	Non diabetics (n = 184)	Diabetics (n = 47)	p
	Demographics
	Age (years)	62.2 ± 15.9	61.6 ± 16.9 64.5 ± 11.0 0.65
	Female	131 (56.7)	96 (52.2)	35 (74.5)	0.006
	Lifestyle
	Alone	78 (35.0)	59 (33.3)	19 (41.3)	0.31
	In institution	4 (1.8)	4 (2.2)	0 (0.0)	0.58
	Comorbidities
	Diabetes	47 (20.3)	-	-	-
	Cardiovascular diseases	97 (42.0)	71 (38.6)	26 (55.3)	0.04
	Respiratory diseases	54 (23.4)	39 (21.2)	15 (31.9)	0.12
	Autoimmune diseases	27 (11.7)	19 (10.3)	8 (17.0)	0.20
	Gastro-intestinal diseases	108 (46.8)	80 (43.5)	28 (59.6)	0.048
	Psychiatric diseases	27 (11.7)	24 (13.0)	3 (6.4)	0.20
	Oncological diseases	82 (35.5)	62 (33.7)	20 (42.6)	0.26
	Urogenital diseases	43 (18.6)	33 (17.9)	10 (21.3)	0.60
	Liver transplant	26 (11.3)	13 (7.1)	13 (27.7) 0.0001
	Malabsorption or digestion	38 (16.5)	29 (15.8)	9 (19.1)	0.58
	Cancer	111 (48.1)	92 (50.0)	19 (40.4)	0.24
	Chemotherapy
	current	28 (12.1)	25 (13.6)	3 (6.4)	0.34
	recent history	13 (5.6)	11 (6.0)	2 (4.3)
	Substance use
	Smocking				0.15
	current	55 (25.7)	44 (26.0)	11 (24.4)
	former	18 (8.4)	11 (6.5)	7 (15.6)
	Use of alcohol				0.18
	current	36 (15.9)	25 (13.9)	11 (23.4)
	former	14 (6.2)	10 (5.6)	4 (8.5)
	Use of other substances				0.23
	current	6 (2.6)	6 (3.3)	0 (0.0)
	former	2 (0.9)	1 (0.6)	1 (2.1)
	Admission
	Reason				0.41
	digestive cancer	24 (10.4)	21 (11.4)	3 (6.4)
	nondigestive cancer	12 (5.2)	11 (6.0)	1 (2.1)
	non-oncologic digestive surgery	64 (27.7)	47 (25.5)	17 (36.2)
	oncologic digestive surgery	41 (17.7)	34 (18.5)	7 (14.9)
	non-oncologic non-digestive surgery	17 (7.4)	15 (8.2)	2 (4.3)

Oral Nutritional Supplements; PN, Parenteral Nutrition; SEFI®, Simple Evaluation of Food Intake®. Qualitative data are presented as n (%), and quantitative data as mean ± standard deviation.

Table 2 -

 2 Description of diabetic population.

	26.3±6.4 kg/m² vs 22.8±6.3 kg/m², p=0.0013). Unexpectedly, diabetic patients had higher FFMI
	(15.5±4.1 vs 17.9±3.4 kg/m², p < 0.0001) with lower bioimpedance parameters (50-kHz resistance and
	50-kHz reactance). The phase angle was not significantly different. In contrast, more of them had low
	handgrip strength (64.3% vs 45.6%, p=0.03). Diabetes was not significantly associated with malnutrition
	or its severity. During follow-up, they developed bacteremia or abdominal infection more frequently,
	while the rate of infection of all types was not significantly higher.
	Variables	Population
	Diabetes characteristics
	Type 1	3 (6.4)
	Type 2	32 (68.1)
	Secondary corticoid	1 (2.1)
	Secondary liver disease	1 (2.1)
	Immunosuppressive secondary	3 (6.4)
	Secondary pancreatic disease	7 (14.9)
	Discovery	5 (10.6)
	Uncontrolled	23 (48.9)
	Last HbA1c (%)	6.4 ± 1.9
	Treatment
	Oral antidiabetics	14 (29.8)
	Oral antidiabetics + insulin	5 (10.6)
	No treatment	8 (17.0)
	Insulin multi-injection	17 (36.2)
	Only basal insulin	2 (4.3)
	Insulin pump	1 (2.1)
	Complications	14 (30.4)
	Type of complications
	Macrovascular	7 (50)
	Retinopathy	5 (35,8)
	Severe	2 (14,3)
	Neuropathy	7 (50)
	Severe	1 (7,1)
	Nephropathy	2 (14,3)
	Number of complications
	1	8 (57, 1)
	2	4 (28,6)
	3	1 (7, 1)
	4	1 (7, 1)
	Severe hypoglycemia < 1 year	0 (0.0)

Table 3 -

 3 Description of the malnourished population. , Body Fat Mass Index; BMI, Body Mass Index; EN, Enteral Nutrition; FFMI, Fat-Free Mass Index; ONS, Oral Nutritional Supplements; PN, Parenteral Nutrition; SEFI®, Simple Evaluation of Food Intake®. Qualitative data are presented as n (%), and quantitative data as mean ± standard deviation.

	Variables	Non-malnourished (n = 38)	Malnourished (n = 193)	p
	Demographics
	Age (years)	57.4 ± 16.9	63.1 ± 15.6	0.06
	Male	16 (42.1)	84 (43.5)	0.87
	Lifestyle: alone
	Alone	8 (22.9)	70 (37.2)	0.10
	In institution	0 (0.0)	4 (2.1)	1.00
	Comorbidities
	Diabetes	8 (21.1)	39 (20.2)	0.91
	Cardiovascular diseases	18 (47.4)	79 (40.9)	0.46
	Respiratory diseases	7 (18.4)	47 (24.4)	0.43
	Autoimmune diseases	8 (21.1)	19 (9.8)	0.06
	Gastro-intestinal diseases	19 (50.0)	89 (46.1)	0.66
	Psychiatric diseases	4 (10.5)	23 (11.9)	1.00
	Oncological diseases	12 (31.6)	70 (36.3)	0.58
	Urogenital diseases	2 (5.3)	41 (21.2)	0.02
	Liver transplant	4 (10.5)	22 (11.4)	1.00
	Malabsorption or digestion	6 (15.8)	32 (16.6)	0.90
	Cancer	14 (36.8)	97 (50.3)	0.13
	Chemotherapy			0.77
	current	3 (7.9)	25 (13.0)
	recent history	2 (5.3)	11 (5.7)
	Substance use
	Smocking			0.73
	current	9 (25.7)	46 (25.7)
	former	4 (11.4)	14 (7.8)
	Use of alcohol			0.56
	current	4 (10.8)	32 (16.8)
	former	3 (8.1)	11 (5.8)
	Use of other substances			0.48

BFMI

Table 4 -

 4 Phenotypic criteria for the diagnosis of malnutrition.

	Variables	Malnourished patients
	Low BMI	56 (29.0)
	Loss of weight	118 (62.4)
	Low FFMI	123 (64.4)
	Low strip strength	95 (58.6)
	Number of positive phenotypic criteria
	1	71 (36.8)
	2	65 (33.7)
	3	37 (19.2)
	4	20 (10.4)

BMI, Body Mass Index; FFMI, Fat-Free Mass Index; Data are presented as n (%).

Table 5 -

 5 Univariate and multivariate analyses of factors associated with mortality.

	Variables	Univariate analysis OR [IC95%]	p	Multivariate analysis OR [IC95%] p
	Demographics
	Age	1.00 [0.97 ; 1.02]	0.91
	Female	0.96 [0.42 ; 2.18]	0.92
	Lifestyle
	Alone	1.19 [0.51 ; 2.75]	0.69
	Institution	0.00 [0.00 ; I]	0.98
	Comorbidities
	Diabetes	2.33 [0.96 ; 5.62]	0.06	[0.96 ; 5.62] 2.33	0.06
	Cardiovascular diseases	2.05 [0.89 ; 4.68]	0.09
	Respiratory diseases	1.88 [0.79 ; 4.51]	0.16
	Autoimmune diseases	0.98 [0.27 ; 3.52]	0.98
	Gastro-intestinal diseases	0.82 [0.36 ; 1.86]	0.63
	Psychiatric diseases	1.44 [0.46 ; 4.54]	0.54
	Oncological diseases	1.15 [0.50 ; 2.68]	0.74
	Urogenital diseases	1.05 [0.37 ; 2.95]	0.93
	Liver transplant	1.51 [0.48 ; 4.79]	0.48
	Cancer	1.30 [0.57 ; 2.94]	0.53
	Chemotherapy		0.81
	Current	1.34 [0.42 ; 4.24]
	Recent history	0.67 [0.08 ; 5.42]
	Malabsorption or digestion	0.63 [0.18 ; 2.23]	0.48
	Substance use
	Smocking		0.82
	Current	1.33 [0.53 ; 3.31]
	Former	0.98 [0.21 ; 4.64]
	Use of alcohol		0.94
	Current	1.20 [0.42 ; 3.42]
	Former	0.00 [0.00 ; 8.13E275]
	Use of others toxic substance		0.93
	Current	1.55 [0.17 ; 13.83]
	Former	0.00 [0.00 ; I]
	Admission
	Reason for admission		0.006
	digestive cancer	1.16 [0.19 ; 7.29]
	nondigestive cancer	4.07 [0.84 ; 19.74]
	non-oncologic digestive surgery	20.33 [4.02 ; 102.73]
	oncologic digestive surgery	1.04 [0.17 ; 6.52]
	non-oncologic non-digestive surgery 2.71 [0.42 ; 17.70]
	oncologic non digestive surgery	4.07 [0.60 ; 27.46]
	digestive disease	5.35 [1.17 ; 24.49]
	others	1
	Admission department		0.02
	Digestive surgery	1
	Intensive Care Unit	0.00 [0.00 ; I]
	Others surgery departments	3.44 [1.04 ; 11.39]
	Medicine department	4.07 [1.49 ; 11.13]
	Anthropometry
	Weight measurement at admission	1.26 [0.36 ; 4.48]	0.72
	(yes vs no)
	Weight kinetics
	Weight at admission	1.26 [0.36 ; 4.48]	0.72
	BMI at admission	0.97 [0.90 ; 1.04]	0.41

  Moreover, the Prospective Urban-Rural Epidemiology (PURE) study, a large longitudinal study conducted in 17 countries and enrolling 142 861 participants has shown that handgrip strength was inversely associated with all-cause mortality (hazard ratio per 5 kg reduction in grip strength 1•16, 95% CI 1•13-1•20; p<0•0001) [41]. In our study, despite a greater fat-free mass, diabetic patients had a lower handgrip strength. The same result was found by Park et al. in the Health

  has shown that increased food intake reduces readmission rate to hospital. They included 206 newly hospitalized adults in Internal Medicine Departments in a prospective open non-randomized controlled study. They worked with the Paul Bocuse Institute to improve the presentation of meal, without changing the ingredients. The experimental group left on their plate less starch (0.19±0.30 vs. 0.52±0.41, p<0.05) and less from the main course than the control group (0.18±0.31 vs. 0.46±0.41, p<0.05). Readmission rate decreased significantly in the study group (p<0.02), from 31.5% to 13.5%.

  It has retrospectively compared all the diabetic people in Ontario, Canada with matched people without diabetes (n=513,749 in each group). It has shown a significant higher rate of infection in people with diabetes (1.21 [99% CI 1.20 -1.22], p<0.0001). Rate was especially high for sepsis (2.45 [2.23-2.68], p<0.0001), post-operative infection (2.02 [1.80-2.27], p < 0.0001) and peritonitis (1.94 [1.58-2.37], p<0.0001). Risk of death attributable to infection was increased with a risk ratio of 1.92 ([1.79 -2.05], p<0.0001) [57]. In 2016, a large observational study based on the Royal College of General Practitioners Research and SurveillanceCentre database in UK has investigated the impact of type 2 diabetes on presentation with infection, and the effect of glycemic control, including 34'278 people with type 2 diabetes and 613'052 people without[START_REF] Hine | Association between glycaemic control and common infections in people with Type 2 diabetes: a cohort study[END_REF]. They found that poor glycemic control (HbA1c>8.5%) increased adjusted odds ratio for all type of infection (1.35 |1.25 -1.45], p < 0,001 vs 1.11 [1.05 -1.18] p < 0,001 for moderate glycemic control).

Remerciements

Loss of skeletal muscle mass

The EWGSOP2 suggest several methods to evaluate the loss of skeletal muscle mass. Magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) are considered gold standards, but the equipment costs and their lack of portability makes difficult their use in clinical routine. They are mostly used in research [START_REF] Bahat | Sarcopenia: revised European consensus on definition and diagnosis[END_REF]. We choose to evaluate the loss of skeletal muscle mass by BIA as it offers a simple, high feasible, easy, cheap, and fast method to assess muscle mass at patient's bed. Decreased of muscle mass measured by BIA is known to be associated to higher risk of malnutrition in hospitalized patients. In 2017, the EMPOWER study, including 378 hospitalized patients aged 70 years or older in Amsterdam studied the association between the risk of malnutrition at admission and the muscle mass. The authors showed that high risk of malnutrition, determined by the Short Nutritional Assessment Questionnaire (SNAQ), was significantly associated with lower muscle mass, assessed by BIA (Odds Ratio, 95% CI, 0.90, 0.85-0.96), and lower appendicular lean mass assessed by DXA (Odds Ration, 95% CI, 0.89, 0.83-0.96) [START_REF] Pierik | High risk of malnutrition is associated with low muscle mass in older hospitalized patients -a prospective cohort study[END_REF]. Moreover, low FFMI has already been described as a risk factor of mortality [START_REF] Genton | Low fat-free mass as a marker of mortality in community-dwelling healthy elderly subjects[END_REF]. In our study, we report that 64.4 % of malnourished patients had a low FFMI. A Danish study has shown that the association between FFMI and mortality is reverse J-shaped with high mortality for low FFMI and a tendency to level off for high values [START_REF] Bigaard | Body Fat and Fat-Free Mass and All-Cause Mortality[END_REF]. In our study, we were unable to show a relation between FFMI and mortality, as we included almost no control patients, i.e. without malnutrition. This is due to our study design leading to the recruitment of malnourished patients mostly.

We showed that diabetic patients had higher FFMI than the non-diabetic patients. This result was not expected. As shown above, diabetes is mainly associated with low muscle mass. Park et al. [START_REF] Park | Excessive Loss of Skeletal Muscle Mass in Older Adults With Type 2 Diabetes[END_REF] described in older type 2 diabetic patients an excessive loss of appendicular lean mass by DXA but the study has only included an older sarcopenic population.

To our knowledge, only some studies have used the BIA to assess the muscle mass of diabetic patients. Shah et al. have performed BIA in 78 type 2 diabetic patients and an equal number of controls.

They found lower values of skeletal muscle mass in diabetic patients compared to controls (23.4%±4.5 vs 30.7%±6.6, p<0.0001). They did not use the FFMI as we did. As their study was published before the GLIM criteria were, the authors did not use thresholds to define a low muscle mass, making the comparison with our results difficult. Using BIA, Sugimoto et al. [START_REF] Sugimoto | Hyperglycemia in nonobese patients with type 2 diabetes is associated with low muscle mass: The Multicenter Study for Clarifying Evidence for Sarcopenia in Patients with Diabetes Mellitus[END_REF] showed that higher HbA1c level (>6,5%) was associated with lower skeletal mass index. The mean HbA1c of our diabetic population was 6.4±1.9%, which may explain why we did not find any association between the FFMI and diabetes.

Our diabetic patients were more often over-weighed or obese than the non-diabetic patients. A German study conducted in 2019 in 175 healthy subjects showed that overweight and obesity were associated with more errors in BIA interpretation. Fat-free mass (FFM) and muscle mass were especially overestimated in obesity [START_REF] Jensen | Limitations of Fat-Free Mass for the Assessment of Muscle Mass in Obesity[END_REF], so that we need specific equations to evaluate fat-free mass in obese patients. We did not use specific equations for obese patients to estimate FFM in our study. The BIA equations we used are not known as the manufacturer keep them secret. Thus, the FFM variable was automatically calculated. These equations may not be appropriate for obese and overweight population, Résultats : 231 patients ont été inclus, dont 20,3% de patients diabétiques et 83,5% de patients dénutris. Les patients diabétiques n'étaient pas plus sévèrement dénutris que le reste de la population (83,0 % vs 83,7%, p = 0,91) mais ils avaient un indice de masse maigre mesuré par bioimpédancemétrie augmenté (15.5±4.1 vs 17.9±3.4 kg/m², p < 0.0001) mais une force musculaire diminuée (64.3% vs 45.6%, p=0.03). En analyse multivariée le diabète était associé à une mortalité plus élevée (OR = 2.33 [0.96 ; 5.62], p=0,06). Discussion : La dénutrition est fréquente chez les patients diabétiques. Le phénotype de leur dénutrition étant particulier, la définition de seuils spécifiques pourrait permettre une meilleure prise en charge.

Abstract :

Introduction: Malnutrition is a major health issue in hospitals because it is associated with more complications and increased mortality. Diabetic patients are at increased risk for malnutrition. However, few studies have investigated the phenotype of malnutrition in this population. Methods: We conducted a retrospective observational study including patients followed by the Nutritional Support Team of CHU Rennes. Results: 231 patients were included, of whom 20.3% were diabetic patients and 83.5% were undernourished patients. Diabetic patients were not more severely undernourished than the rest of the population (83.0 % vs 83.7%, p = 0,91) but had increased muscle mass (15.5±4.1 vs 17.9±3.4 kg/m², p < 0.0001) but decreased strength (64.3% vs 45.6%, p=0.03). In multivariate analysis, diabetes was associated with increased mortality (odds ratio = 2.33 [0.96 ; 5.62], P=0,06). Discussion: Malnutrition in frequent in diabetic patients. The phenotype of malnutrition being particular, the definition of specific thresholds could allow a better management.


Jefferson Mortatti

Jean-Luc Probst

Helder De Oliveira

Christophe Hissler

Alexandre Martins Fernandes

João Paulo

Rambelli Bibian

Renato Alessandro Lopes

SPATIAL VARIATION OF TRACE ELEMENTS IN THE TIETÊ RIVER BOTTOM SEDIMENTS (SÃO PAULO, BRAZIL): ENRICHMENT FACTORS AND ANTHROPOGENIC CONTRIBUTIONS

The spatial variation of some trace elements (Ni, Cr, Zn, Cu, Sc, Co, Rb, Sr, Cs, Pb, Th and U) was studied in the bottom sediments of the Tietê river in São Paulo, Brazil. The main objective was to identify the enrichment factors and possible anthropic contributions to the observed high toxicity of these chemical elements in this drainage basin, which crosses the metropolitan area of São Paulo city with about 20 millions people and receives a large load of domestic and industrial wastes. The chemical analyses were performed after the chemical extraction procedure using alkaline fusion with Lithium tetraborate. An Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) and a Neutron Activation Analysis (NAA) were used for the analytical purposes. The enrichment factor was calculated by the relationship between the concentration of each trace element in the bottom sediment and the natural soil background in the drainage basin, weighted by the conservative Sc.

INTRODUCTION

Metallic trace elements mainly the heavy metals are present in different forms in the environment (atmosphere, water, soil, sediments and living organisms). These forms are controlled by the physic and chemical conditions of the environment and in terms of the main elements constituting the ecosystem. The toxicity of some heavy metals in the fluvial ecosystems is related not only to the observed high concentrations but also with their chemical form presents in the water/sediment interface and represent a high sanitary risk for the population and other living organisms by the consume of polluted water and/or food.

According to the literature the high concentrations observed in the main polluted fluvial ecosystems are from domestic and industrial wastes [START_REF] Adriano | Trace Elements in the Terrestrial Environment[END_REF][START_REF] Meybeck | The quality of rivers: from pristine stage to global pollution[END_REF][START_REF] Ebinghaus | Mercury contaminated sites[END_REF] but also from the agriculture [START_REF] Chlopecka | Forms of cadmium, lead and zinc in contaminated soils from southwest Poland[END_REF][START_REF] Baize | Teneurs totales en elements traces métalliques dans les sols (France), Références et stratégies d'interprétation[END_REF]. This is the case of the Tietê river basin located in the São Paulo State, Brazil, which crosses the metropolitan area of São Paulo which is one of the most industrialized and largest urban centers of South America. The whole drainage basin comprises about 20 million people and receives a large load of industrial and mostly domestic wastes.

Hydrogeochemical studies in drainage basins are very important nowadays to understand the behavior of the metallic trace elements in fluvial ecosystems in order to identify and control the critical level of toxicity and to promote the right activities for the agricultural soil management and mainly for the domestic and industrial waste treatment.

The aim of this paper was to know the spatial variation of some trace elements (Ni, Cr, Zn, Cu, Sc, Co, Rb, Sr, Cs, Pb, Th and U) in the bottom sediments of the Tietê river in order to identify the enrichment factors and possible anthropic contributions to the observed high toxicity of these chemical elements in the drainage basin.

STUDY AREA

The Tietê River basin is a unit of the Paraná basin consisting of sub-basins including the Piracicaba, Atibaia, Jaguari, Sorocaba, and Corumbataí river basins, and extends over an area of about 32930 km 2 with regard to the Barra Bonita reservoir. It lies between 21 o 30' and 24 o 00' S and 46 o 00' and 48 o 00' W, being about 260 km west from the Atlantic Ocean coast (Fig. 1). he enrichment factor values placed between 0.5 and 2.0 must be considered as part of the atural variability that occurs in the studied ecosystem [START_REF] Sposito | The chemistry of soils[END_REF]. EF values higher than 5 must be onsidered critical for possible disponibilities and for the incoming risks.

Campinas

C a p iv a r i Rio

Sorocaba

RESULTS AND DISCUSSION

can be observed in Table 1 the main results of trace element concentrations in the bottom ediments of Tietê River basin. industrialized and largest urban centers of South America. The basin under study comprises about 20 million people and receives a large load of industrial and mostly domestic wastes.

The main geological domain is formed by rocks of the crystalline plateau and sedimentary basin, represented basically by granites, gneisses, and basalts mainly in the middle part of the basin along the Depressão Periférica zone. Among the major types of soils that occur in the Tietê River basin, ultisols (55.4 %) and oxisols (28.3 %) cover about 84% of the entire basin area [START_REF] Oliveira | Solos do Estado de São Paulo: descrição das classes registradas no mapa pedológico[END_REF]. The Tietê River basin is characterized by a Cwa clima c basin comprises an area of intense agricultural (sugarcane and citrus) and industrial occupation (paper, cellulose, textile products, and oil refinery).

The Tietê River basin is characterized by a Cwa climate llulose, textile products, and oil refinery).

MATERIALS AND METHODS

The bottom sediments were sampled during January to May 2004 period, a long of the Tietê river, from the headwaters to the mouth using a classical grab sampler and storaged at 4 o C in plastic bags until the chemical analysis. The sediment was sieved (63 m) and for total trace element extraction the alkaline fusion procedure was used with lithium tetraborate and lithium metaborate (3:1) in a platinum crucible under 1000 o C during 30 minutes. The residual phase was taken in 100 mL HNO 3 2N. The chemical analyses were performed using an Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) and a Neutron Activation Analysis (NAA) for Ni, C o basin were sampled at different depths ((0-10 cm, 40-50 cm e 90-100 cm) and the same procedure was followed.

T weighted by the conservative Sc of the samples (Y S ) and the natural background soil Sc

(Y R ) EF = (X S / Y S ) / (X R / Y R ) (1)
T n c

4.

It s The spatial distributions of the main trace elements in the bottom sediments of the Tietê River an be observed in Fig. 2 and3. c he world mean value of 6.0 mg/kg. For Rb and Sr, the same th respect to the domestic and industrial T According to the observed in Fig. 1, Ni, Cr, Zn, Cu and Pb concentrations in the bottom sediments of the Tietê River, have been increased from headwaters (TM1) to Santana do Parnaíba sampling station (TM3), with respectively 648 mg/kg of Zn and 183 mg/kg of Cr, decreasing after in direction to the Barra Bonita reservoir (BB2), with 163 and 94 mg/kg for Zn and Cr respectively. In downstream direction to the sampling station BB3, the Zn and Cr oncentrations have been increased again with values of 514 and 262 mg/kg respec c These values were higher than the observed in the literature for the same reservoir [START_REF] Silva | Heavy metal distribution in recent sediments of the Tietê-Pinheiros river system in São Paulo State, Brazil[END_REF].

High Th and U concentrations were verified in the bottom sediments of the Tietê River near the headwater sampling station at Salesópolis (TM1), showing a natural occurence possibility. The concentrations obtained for Th (163 mg/kg) and U (42 mg/kg) were 10 times higher than the world mean value (14 and 3 mg/kg, respectively) [START_REF] Martin | Elemental mass balance of material carried by major world rivers[END_REF]. For Cs, the observed concentration 7.7 mg/kg) was similar to t ( behavior could be observed.

For all trace elements, it was possible to observe the same behavior of increase concentration near the TM3 and TM4 sampling stations (more populated zone of the basin) and consequently downstream decrease to the Barra Bonita reservoir. It seems to be evident the nthropogenic influences in this área, mainly wi a effluent releasing without any previous treatment. The behavior of the enrichment factors a long of the Tietê river basin calculated for Ni, Cr, Zn, Cu and Pb verified in the bottom sediments, for the studied period, can be observed in g. 4. Considering the classification proposed by Sposito [START_REF] Sposito | The chemistry of soils[END_REF] it was used in this paper the inated bottom ediments. This critical situation remains a long of the basin including the Barra Bonita he enrichment factors calculated for Ni and Pb seems to be from natural occurrence and lustrate the behavior of the enrichment factors calculated a long of the Tietê river asin for Co, Rb, Sr, Cs, Th and U observed in the bottom sediments during the studied eriod. According to the observed for the headwater sampling station (TM1) at Salesópolis, Rb, Sr, Cs and U shows a significative enrichment factors in comparison to the natural background, but it seems to be localized. The distributions observed for Co and Th could be considered normal at this sampling point. The high value observed for U at headwater decrease a long of the basin and remains constant under natural occurrence. The Co distributions increase a long of the basin with an enrichment factor of 6 in the sampling station near of the Barra Bonita reservoir (BB4).

The behavior observed at TM1 sampling station was justified in terms of the organic soils that occur in this region with shows high concentration of this trace elements.

CONCLUSIONS

The results observed a long of the Tietê River basin for the distribution of trace elements in the bottom sediments shows to be preoccupant because it was possible to verify critical points in the basin where anthropogenic influences seems to be very important mainly around the great São Paulo with release of domestic and industrial effluents without previous treatment. The monitoring of the river waters and sediment loads must to be implanted for a better control of the water quality and effluent released and to improve the treatment plants a long of the river.

Figure 1 .

 1 Figure 1. Location map of the Tietê River basin including the sampling stations along of river to the Barra Bonita reservoir.

F T igure 2 .

 2 Concentration of Ni, Cr, Zn, Cu and Pb in the bottom sediments along of the ietê River basin, for the studied period. igure 3. Concentration of Cs, Th, U, Rb F and Sr in the bottom sediments along of the ietê River basin, for the studied period.tively.

Fi enrichment factor 2

 2 to separate the natural contribution from the anthropogenic influences.High enrichment factors associated with Cu and Zn present in the bottom sediments were verified a long of the Tietê River basin. In the headwaters only Zn was higher than 2. After this point the EF values increase until Santana do Parnaíba station (TM3) that involves the urban zone of São Paulo, with values of 15 for both Cu and Zn showing contam s reservoir where Cr shows to be more important with an enrichment factor of 9.T remained constant a long of the basin.

Fig. 5

 5 Fig. 5 il b p

Figure 4 .Figure 5 .

 45 Figure 4. Enrichment factors (EF) calculated for Ni, Cr, Zn, Cu and Pb in the bottom g of the Tietê River basin during the studied period. sediments a lon

Table 1 . Trace element concentrations (mg/kg) for the Tietê River bottom sediments in e main sampling stations a long of the drainage basin, for the studied period.

 1

	th
	S	ample Ni Cr Zn Cu Sc Co	Rb Sr	Sb	Cs	Pb Th	U
	TM1	43 60 88 41 16	8.72	146 119 0.075 7.67 46.1 163 42.0
	TM1b
	TM2	31 114 266 53 19	7.16	92.0 1	15 0.136 6.06 47.5 37.1 11.5
	TM3	92 183	648 200 19	15.9	95.2 134 0.269 5.02 76.5 26.8 7.60
	TM4	60	1	33 360 79 16	13.7	121 78 0.196 6.06 44.3 24.7 6.78
	TM5	67 127 381 97 14	11.9	91.8 84 0.126 5.10 36.0 21.4 6.28
		BB1	41 96 210 45 20	19.3	85.8 62 0.335 6.81 28.0 15.4 3.57
		BB2	70 94 163 39 12	14.3	38.1 43 0.233 5.76 17.9 22.3 5.59
		BB3	51	262	514	55	14	14.3	56.6	69	0.152	2.90	18.5	30.0	7.81
		BB4	80	132	266	46	15	19.4	69.1	42	0.259	5.88	30.3	15.6	5.29
		BB5	237 111 126 109 32	26.0	49.5 56 0.159 5.37 31.2 16.8 3.95

Table 2

 2 shows the calculated enrichment factors for the main trace elements verified in the bottom ediments a long of the Tietê River basin.

	able 2.

Enrichment factors (EF) for the main trace elements occurring in the bottom ediments of the Tietê River, weighted in terms of Sc, for the studied period.

	s
	T
	s
	EF	Ni	Cr	Zn	Cu	Sc	Co	Rb	Sr	Cs	Pb	Th	U
	TM1	1.0	1.8	2.2	3.8	1.0	2.3	5.7	7.6	5.8	0.6	1.7	5.5
	TM1b
	TM2	0.6	2.9	5.7	4.1	1.0	1.6	3.0	6.2	3.9	0.6	0.3	1.3
	TM3	1.8	4.5 13.6 14.9 1.0	3.4	3.1	7.0	3.2	0.9	0.2	0.8
	TM4	1.4	3.9	8.9	7.0	1.0	3.5	4.6	4.8	4.5	0.6	0.2	0.9
	TM5	1.7	4.2 10.7 9.7	1.0	3.4	4.0	5.9	4.3	0.6	0.2	0.9
	BB4	2.0	4.2	7.2	4.5	1.0	5.4	2.9	2.8	4.8	0.4	0.2	0.7
	BB2	2.1	3.6	5.4	4.6	1.0	4.9	1.9	3.6	5.7	0.3	0.3	1.0
	BB3	1.3	8.8 14.8 5.6	1.0	4.2	2.5	5.0	2.5	0.3	0.3	1.2
	BB1	0.8	2.3	4.3	3.2	1.0	4.0	2.7	3.1	4.1	0.3	0.1	0.4
	BB5	2.8	1.7	1.6	5.0	1.0	3.4	1.0	1.8	2.1	0.2	0.1	0.3
	soil nat	50	39	45	13	18	4.4 29.5 18.2 1.5 83.0 112.4 8.8