Rudy Guicheteau

Jean-Louis Bobet

Miyasaki Takamichi

Akira Kawasaki

Yongfeng Lu

Jean-François Silvain
email: jean-francois.silvain@icmcb.cnrs.fr

Takamichi Miyasaki

Characterization of the interface reaction zone between iron and NiZn ferrite in a

Keywords:

Introduction

In recent decades, the development of bulk single phase mag netic materials has mainly focused on the optimization of com posites or nanocomposite materials. These materials are being increasingly investigated for their electromagnetic properties in transformers [START_REF] Kefalas | Transformers made of composite magnetic cores: an innovative design approach[END_REF], generators, motors [START_REF] Ishikawa | Design of a DC motor made of soft magnetic composite core by the experimental design method[END_REF], magnetic amplifiers and microwave absorbers [START_REF] Yan | Broadband and thin microwave absorber of nickel-zinc ferrite/carbonyl iron composite[END_REF][START_REF] Wei | Double-layer microwave absorber based on nanocrystalline Zn0.5Ni0.5Fe2O4/a-Fe microfibers[END_REF]. Different types of organic [5e7] and inorganic [START_REF] Liu | Microwave absorption properties of FCC-Co/Al2O3 and FCC-Co/Y2O3 nanocapsules[END_REF] composites are already available. Composites contain one component with good magnetic properties, while the other is either an insulator [START_REF] Razzitte | Electrical permittivity of Ni and NiZn ferrite-polymer composites[END_REF] and/or a binder [START_REF] Doser | Method of making magnets by polymer-coating magnetic powder[END_REF]. Due to the presence of a non magnetic element, the global magnetic properties (e.g. the permittivity) of the whole composite system decreases. To over come the loss of magnetic properties in the composite, using a soft magnetic material such as Fe or an Fe alloy and soft ferrite [3,4,9e11] instead of a non magnetic material has been investi gated. The magnetic Fe or Fe alloy systems have been reported to have high (i) initial permeability and (ii) saturation magnetization. Owing to their low electrical resistivity and Eddy current losses, they cannot be used at high frequency. On the other hand, soft ferrite allows their usage at high frequencies due to their high electrical resistivity, high permeability, low density and low Ni 0.5 Zn 05 Fe 2 O 4 ferrite particle size of 30 mm. Mixtures of iron þ20 wt % of ferrite particles were prepared and cold pressed at 400 MPa. X ray diffraction (XRD) pattern of the mixed powder before sintering shows the presence of pure ferrite phase with no extra peaks. The final pellet dimensions obtained were 20 mm in diameter and 2 mm in thickness.

Ferrite silica iron deposited film (model material)

Silica thin films about 70 nm thick were deposited by magne tron sputtering, on the polished slide of the ferrite substrate heated at 500 C (see Section 2.1). The deposition was performed at 50 W under 5 Pa of Argon pressure, with a 3 inch diameter silica target, for 15 min. An iron thin film 1 mm thick was then deposited on the silica layer by magnetron sputtering with the same process described in Section 2.1.

Heat treatment

Both model systems were heat treated at 500 C, 650 C, 700 C and 900 C for 1 h, under an inert atmosphere of purified N 2 , in a tubular furnace at a heating rate of 10 C/min. Microstructural characterizations were performed using a scanning electron mi croscope (SEM, TESCAN Vega 3 SB) and a high resolution trans mission electron microscope (HR TEM, HF 200EDX, Hitachi). For TEM analysis, Fe SiO 2 ferrite thin films were prepared using con ventional ion milling method (GATAN PIPS Model 691, Gatan Inc.). Elemental analyses were performed using an electron probe microanalyzer (Cameca SX 100) equipped with three WDS (wave length dispersive spectroscopy) and one SDD (silicon drift detector) spectroscope. X Ray diffraction (XRD) was performed using a Phi lips PANalytical X'Pert (PW1820) diffractometer with Cu K a 1 radiation.

Results

Reference iron/ferrite material

The sintering behavior of the Fe/ferrite composite was analyzed. The variation of the sample length (left part of the y axis) and of the heat flow (right part of the y axis) versus the heating temperature (x axis) is presented in Fig. 1. From the variation of the sample length with temperature, three temperature ranges, correlated with three material behaviors, can be defined as follows:

1. From room temperature to 580 C, an almost linear increase of the sample length was observed and compared with the varia tion of pure iron powders (red line). As both curves were very close, this linear increase can be mainly attributed to the ther mal expansion of the iron part of the Fe/ferrite mixed powder materials. In this temperature range chosen, a small and rather broad exothermic peak was observed. It can be attributed to an increase of the ferrite crystallites size that may be previously reduced during the crushing of the pellet Ferrite materials. This hypothesis was well supported by the XRD pattern analysis of the mixed Fe/Ferrite powders after a heat treatment at 500 C (Fig. 2 500 C). Indeed, XRD analysis does not show any addi tional phase (compared with the XRD pattern of mixed pow ders) but highlights a decrease in the full width at half maximum (FWHM) of the peaks of the ferrite phase. SEM mi crographs (Fig. 3a) of typical Fe/ferrite show the absence of a reaction zone between the ferrite and the Fe powders. 2. From 580 C to 650 C, the variation in the sample length was almost negligible. In this temperature range, the dilatation of the composite material was counterbalanced by the contraction of the material associated with the beginning of the sintering process. An intense endothermic peak (Fig. 1) was observed in the temperature range studied. This endothermic peak may be associated with the transformation of the ferrite phase into three new components: FeO, Fe 1-x Ni x and Fe 1-x Zn x O, as shown in the XRD pattern taken at 600 C (Fig. 2 600 C). After annealing at 700 C, a reaction zone almost 6 mm in depth composed of white stripes and dots was present at the ferrite Fe interface (see SEM micrograph in Fig. 3b) in contrast to samples annealed at 500 C. The exact composition of the reaction zone was not confirmed, but could be associated with the initial transformation of the ferrite phase with temperature. 3. Above 650 C, a sharp decrease in the sample length was observed. This decrease has to be attributed to the sintering of the mixed powder materials. In this temperature range, a small endothermic peak, at 760 C was observed and may be attrib uted to sublimation of the Zn species. At these temperatures, the ferrite phase is fully transformed into FeO, Fe 1-x Ni x and Fe 1-

x Zn x O, as shown in the XRD patterns recorded after heating ). The fourth one, which can be attributed to the FeNiO phase, was present at the boundary between Fe 1-x Ni x and FeZnO (green contrast in the oxygen map). This phase could not be detected by XRD due to its low content and poor crystallinity.

Model ferrite iron material

The Fe/ferrite composite materials fabricated by powder met allurgy with Fe and ferrite powders, with a PVD iron thin film deposited onto polish bulk ferrite substrate, were annealed under A controlled atmosphere (N 2 ) at three different temperatures (i.e. 500 C, 700 C and 900 C). SEM microstructures and corre sponding EDS elemental maps were recorded for the three annealing temperatures.

Fig. 5 presents the typical SEM cross section microstructures recorded on polished samples. Fig. 5a shows that, up to 500 C, no evolution of the microstructure or chemical interfacial reaction between the iron film and the ferrite substrate was observed. On the contrary, for the Fe/ferrite composite materials, chemical interfacial reactions were easily observed at higher annealing temperatures (Fig. 5b for 700 C and Fig. 5c for 900 C). As expected, the thickness of these complex reaction zones increased with the annealing temperature, from about 14 mm (at 700 C) to nearly 36 mm (at 900 C). At 700 C, the interfacial reaction zone can be divided into two distinct parts from the iron surface to the ferrite bulk: (i) the first one, called d1 in Fig. 5b (thickness close to 6 mm) with white stripes SEM contrasts and (ii) the second one, called d2 in Fig. 5b (thickness close to 8 mm) with homogeneously distributed white dots. EDS elemental cartography of the typical diffusion zone can be seen in Fig. 6, which includes both an SEM micrograph and the Fe, O, Ni and Zn elemental maps. As for Fig. 4, the atomic content of the four maps, which can be associated with the color map, has to be visualized on the right side of each elemental maps. Dashed blue and red lines on the SEM micrograph and EPMA maps (Fig. 6) correspond to the initial iron film (1 mm thick) and the diffusion zone analyzed with the elemental maps corresponding to the d1 zone (close to 6 mm) (Fig. 5b), respectively. It has to be mentioned that the black circular contrast, visible on the SEM mi crographs, was associated with porosities. In the 6 mm diffusion zone, the oxygen and zinc contents were close to 40 and 5 at. %, respectively. The low oxygen and zinc atomic concentrations compared with the bulk (50 and 10 at. %) shows that oxygen and zinc diffused homogeneously during the heat treatment from the ferrite bulk substrate toward the PVD iron layer. This diffusion process may be associated with the reverse diffusion of iron and the depletion of nickel.

At 900 C, the d 0 1 zone was detected (Fig. 5c) and associated with the d 0 2 zone (thickness close to 30 mm). The elemental atomic concentration of this d 0 1 zone, presented in Fig. 7, is very close to that of the d1 zone observed after annealing at 700 C. However, inside the d 0 2 zone, an evolution of the elemental composition of the ferrite grain boundaries was observed. Indeed, a depletion of iron associated with the enrichment of both nickel and zinc was clearly visible. As for Fig. 4, the atomic content of the four maps, which can be associated with the color map, can be visualized on the right side of each elemental map.

Model ferrite SiO 2 iron material

In order to kinetically reduce the chemical reaction that occurred above 500 C between iron and ferrite during annealing, an interphase layer was intercalated between Fe and ferrite. From the literature [START_REF] Darken | Melting points of iron oxides on silica; phase equilibria in the system Fe-Si-O as a function of gas composition and temperature[END_REF][START_REF] Muan | Phase equilibria in the system FeO-Fe 2 O 3 -SiO 2[END_REF], it was established that silica is chemically inert towards iron and magnetite at temperature up to 900 C under a controlled atmosphere (N 2 ). Hence, from these previous results, SiO 2 thin films (thickness close to 50 nm) were PVD deposited onto the polished ferrite substrate prior to PVD iron film deposition. One hour heat treatments were then performed at 700 C and 900 C, under an N 2 atmosphere, on PVD Fe PVD SiO 2 ferrite multilayer materials. Fig. 8 shows SEM (Fig. 8a) and TEM (Fig. 8b) cross sectional micrographs of the multilayer material annealed at 700 C for 1 h. Both SEM and TEM micrographs clearly demonstrate that the SiO 2 thin film had not chemically reacted with either iron or ferrite during the annealing process. In Fig. 8b, nano diffraction patterns recorded from ferrite, SiO 2 and iron are shown. The indexations of these diffraction patterns was done using the space group and lattice parameter of each phase (e.g. ferrite: S.G. Fd 3m with a 8.40 Å, SiO 2 : amorphous and Fe: S.G. Im 3m with a 2.89 Å).

In contrast to the behavior of the multilayer material annealed at 700 C, annealing at 900 C clearly shows that a chemical reac tion had occurred (see Fig. 9a (SEM micrograph) and 9b (TEM micrograph)). Indeed, both SEM and TEM analyses show that the entire Fe film had diffused inside the ferrite substrate, through the SiO 2 layer, leading to the formation of a thin Fe 2 SiO 4 layer (fayalite:

Discussion

The Ni Fe O ternary phase diagram [START_REF] Dalvi | Diffusion model for oxidation of nickel-iron alloys at 1000 C[END_REF] indicates that, at 1000 C, Ni and FeO (with higher FeO content) are not chemically stable and react to form an FeNi FeO solid solution. Our results show that a redox reaction occurs above 600 C. The reaction zone (shown by dotted line Figs. 6 and7) was similar (about 13 mm) after heat treatment at 700 C or 900 C. Moreover, at 900 C, the oxygen content was almost constant throughout the sample (around 45%). It is then possible to infer that such diffusion was limited by the redox reaction and by the diffusion of iron in the ferrite, forming the Fe(Zn)O phase highlighted by XRD.

Therefore, after heat treatment at 900 C, there was no zinc into the reaction zone and in the two model systems, there was segre gation of nickel at the grain boundaries.

From the abovementioned results, we can suggest the following mechanism (cf. Fig. 10):

Metallic iron (Fe 0 ) is oxidized by iron (III) and nickel (II) to form iron (II) and metal nickel according to the following reactions:

Ni 2þ þ Fe 0 (s) 4 Fe 2þ þ Ni 0 (s) 2 Fe 3þ þ Fe 0 (s) 4 3 Fe 2þ
This oxidation can be correlated with the exothermic peak starting around 580 C (reported on Fig. 1). This reaction leads to the formation of FeO and ZnO phases. Moreover, the Zn Fe O ternary phase diagram [START_REF] Gleitzer | Diversity and complexity of the wustite solid solutions I-tentative rationalization of the miscibility data and classification of the wustite ternary fields and of the post saturation reactions[END_REF] indicates the high stability of both FeO and ZnO. The possible formation of a Fe(Zn)O solid solution (cf. Fig. 10a) has also been reported.

Metallic iron diffuses through the FeO phase to form an Fe 1-x Ni x (cf. Fig. 10b) solid solution. A redox reaction takes places with two possible results:

If iron is the major phase, there is enough iron in Fe 1-x Ni x phase to reduce all Ni 2þ in the ferrite phase. By repeating steps (a) and (b), the material presents stripes (cf. Fig. 10c). If NiZn ferrite is the major phase, the iron present in Fe 1-x Ni x phase reduces only part of the Ni 2þ in ferrite phase, forming dots (cf. Fig. 10d). Redox and diffusion lead to total consumption of the minor phase. By sintering the composite at a temperature above 800 C under low oxygen pressure, Zn sublimation occurs [START_REF] Hansson | Phase equilibria in the Fe-Zn-O system at conditions relevant to zinc sintering and smelting[END_REF].

Sintering temperature of the Fe ferrite composite can be enhanced to 800 C by adding a 70 nm SiO 2 interphase. At higher temperatures, Fe and SiO 2 react to form crystallized fayalite. Metallic iron in excess can diffuse into the ferrite substrate and consume part of it. Therefore, the SiO 2 layer cannot be used as diffusion barrier for treatment at temperatures above 800 C (un der a neutral N 2 atmosphere). It should be mentioned that this new architecture of the multi material model, linked with the interca lation of an SiO 2 layer, should be confirmed on ferrite material fabricated by powder metallurgy. For the geometry of such mate rial, PVD coating of a powder is not relevant and optimization of a chemical coating on liquid media would be required.

Conclusion

Fe/NiZn ferrite materials were fabricated and heat treated in order to understand the chemical reactions that can take place between oxide spinel and iron. Three different systems were studied. In the first one, iron films were PVD deposited onto an NiZn ferrite substrate. In the second one, iron/ferrite materials were fabricated using a powder metallurgy process. In the third one, an SiO 2 layer was deposited, by PVD, onto the NiZn ferrite substrate, followed by PVD iron deposition in order to form a bulk NiZn ferrite substrate/PVD SiO 2 /PVD Fe system. ATD analyses showed an oxido reduction phenomenon starting at 580 C. This oxido reduction occurred between Ni 2þ , Fe 3þ and Fe at the inter phase between iron and ferrite. This reaction was followed by the disproportionation of FeNi and FeO and also by iron diffusion, leading to total consumption of the minor initial phase. The ZnO phase exhibited good stability during heat treatment, but at tem peratures above 800 C, sublimation occurred due to low the oxy gen content in the atmosphere. These experimental results obtained with the model materials show that sintering must be done at a temperature lower than 580 C in order to avoid the chemical reaction at the NiZn ferrite Fe interface and therefore the creation of complex interphases and finally possible degradation of the physical (namely magnetic) properties of the system. In order to delay this oxido reduction phenomenon in NiZn ferrite Fe systems above 580 C, an SiO 2 thin film was deposited in between these two materials. For this bulk NiZn ferrite substrate/PVD SiO 2 /PVD Fe model system, the sintering temperature could be increased up to 800 C without any interfacial chemical reaction. Above 800 C, a reaction occurred between iron and the silica layer to form an iron silicate, Fe 2 SiO 4 (i.e. fayalite).

Conflicts of interest

The authors whose names are listed immediately below certify that they have no affiliations with or involvement in any organi zation or entity with any financial interest (such as honoraria, educational grants, participation in speakers' bureaus, member ship, employment, consultancies, stock ownership, or other equity interest, and expert testimony or patent licensing arrangements), or non financial interest (such as personal or professional re lationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Author names: Rudy GUICHETEAU, Jean Louis BOBET, Takamichi MIYASAKI, Akira KAWASAKI, YongFeng. LU, Jean François SILVAIN.

Fig. 3 .

 3 Fig. 3. SEM micrographs of Fe-Ferrite mixed powders, heat treated at (a) 500 C, (b) 700 C and (c) 900 C (BSE mode).

Fig. 4 .Fig. 5 .

 45 Fig. 4. EPMA element cartography of Fe, Ni, O, Zn on ferrite mixed powders after treatment at 900 C under N 2 .

Fig. 6 .

 6 Fig. 6. SEM micrograph and corresponding EPMA element cartography of Fe, Ni, O, Zn on Fe-ferrite film after heat treatment at 700 C under N 2 .

Fig. 7 .

 7 Fig. 7. SEM micrograph and corresponding EPMA element cartography of Fe, Ni, O, Zn on Fe-ferrite film after heat treatment at 900 C under N 2 .

Fig. 8 .

 8 Fig. 8. SEM micrograph (a) and TEM micrograph (b) of Fe-SiO 2 -ferrite system heat treated 1 h at 700 C under N 2 .

Fig. 9 .

 9 Fig. 9. SEM micrograph (a) and TEM micrograph (b) of Fe-SiO2-ferrite system heat treated 1 h at 900 C under N 2 .


Benjamin Landré

Sébastien Czernichow ",$,% Marcel Goldberg

Marie Zins !,%

Joël Ankri !,'

Marie Herr !,'

Marie Herr
email: marie.herr@uvsq.fr.

Association between life-course obesity and frailty in older adults: findings in the GAZEL cohort

Keywords: frailty, epidemiology, life-course

Objective:

This study aimed to assess the relationship between weight history during adulthood and frailty in late life, in men and women participating in the GAZEL cohort.

Methods:

This cohort study included 8 751 men and 3 033 women (aged 61 to 76 years) followed-up since 1989. Modified Fried's frailty criteria (weakness, fatigue, unwanted weight loss, low physical activity, and impaired mobility) were assessed in 2015. Reported body mass index (BMI) was determined each year to characterize: obesity status in 2015, obesity duration over the 1990-2015 period, and trajectories of BMI during. Associations between frailty and weight history were assessed using multinomial regression.

Results:

In 2015, 12% of men had obesity, 1.8% severe obesity and 0.4% morbid obesity; 11%, 2.2% and 0.8% respectively for women. Individuals with obesity were more likely to be frail than those with normal BMI and the risk of frailty increased with each additional year of obesity (1.04 [1.00-1.08] for men and 1.07 [1.02-1.13] for women). Trajectories of BMI revealed that both long-term obesity and onset of obesity in late adulthood are associated with frailty.

Conclusions:

Current and past obesity appear to be important determinants of frailty. Early weight management may be beneficial in old age.

What is already known about this subject?

-Frailty is a predictor of poor health outcomes among older adults.

-Although malnutrition is part of frailty, previous studies demonstrated that aged individuals with obesity are more likely to be frail compared to aged individuals without obesity.

-The impact of life-course obesity on the risk of frailty in old age remains poorly described.

What does this study add?

-In a cohort of older adults followed for 26 years, we showed that the duration of obesity influenced the risk of frailty: the longer the period of obesity, the higher the risk of frailty.

-We also identified a persisting effect of obesity on the risk of frailty among previously obese individuals.

-Finally, we identified three trajectories of body mass index (BMI) over adulthood: normal or overweight / constantly high / increasing BMI, of whom the last two were associated with increased risk of frailty.

Introduction

Frailty is an indicator of decreased physiologic resources and resistance to stressors [START_REF] Clegg | Frailty in elderly people[END_REF] . It can be used to identify a category of older individuals who are no longer robust (non-frail) but not disabled, and who are likely to experience poor health outcomes [START_REF] Vermeulen | Predicting ADL disability in community-dwelling elderly people using physical frailty indicators: a systematic review[END_REF][START_REF] Vermeiren | Frailty and the Prediction of Negative Health Outcomes: A Meta-Analysis[END_REF][START_REF] Kojima | Frailty as a Predictor of Future Falls Among Community-Dwelling Older People: A Systematic Review and Meta-Analysis[END_REF][START_REF] Kojima | Frailty as a predictor of fractures among community-dwelling older people: A systematic review and meta-analysis[END_REF] . Because weight loss forms part of several definitions of frailty [START_REF] Buta | Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments[END_REF] , frailty is often associated with underweight. However, overweight and obesity have been found to be associated with frailty [START_REF] Blaum | The Association Between Obesity and the Frailty Syndrome in Older Women: The Women's Health and Aging Studies[END_REF] , challenging the idea that overweight or obesity could represent a protective physiologic reserve. Furthermore, obesity has been suggested as a risk factor for frailty among the elderly [START_REF] Hirani | Longitudinal associations between body composition, sarcopenic obesity and outcomes of frailty, disability, institutionalisation and mortality in community-dwelling older men: The Concord Health and Ageing in Men Project[END_REF][START_REF] Soysal | Inflammation and frailty in the elderly: A systematic review and meta-analysis[END_REF] .

Obesity has reached epidemic proportions in industrialized countries and may increase the medical burden of aging populations in a very near future. Indeed, obesity has been associated

with numerous comorbidities, such as type 2 diabetes, cardiovascular disease [START_REF] Mahajan | Impact of obesity on cardiac metabolism, fibrosis, and function[END_REF] , osteoarthrosis [START_REF] Thijssen | Obesity and osteoarthritis, more than just wear and tear: pivotal roles for inflamed adipose tissue and dyslipidaemia in obesityinduced osteoarthritis[END_REF] , and cancer [START_REF] Juo | Obesity Is Associated with Early Onset of Gastrointestinal Cancers in California[END_REF] . Obesity also contributes to low-grade chronic inflammation [START_REF] Soysal | Inflammation and frailty in the elderly: A systematic review and meta-analysis[END_REF] and tissue remodelling [START_REF] Reis | Association of the degree of adiposity and duration of obesity with measures of cardiac structure and function: The CARDIA study[END_REF] . Because the adverse effects of obesity may vary depending on the period and time of exposure, understanding the relationship between obesity and frailty requires a life-course approach.

Stenholm et al. [START_REF] Stenholm | Midlife Obesity and Risk of Frailty in Old Age During a 22-Year Follow-up in Men and Women: The Mini-Finland Follow-up Survey[END_REF] actually showed that midlife history of excess weight was predictive of frailty 22 years later. Among men, Strandberg et al. [START_REF] Strandberg | Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men[END_REF] highlighted that weight gain during adulthood was associated with an increased risk of frailty in old age. Using a 10-year follow-up of weight, Mezuk et al. [START_REF] Mezuk | Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study[END_REF] identified four trajectories of weight evolution among men included in the Health and Retirement Study. The results indicated that, compared to the reference trajectory (consistently overweight), all other trajectories were associated with an increased risk of frailty, especially in the case of weight gain. Recently, Haapanen et al. [START_REF] Haapanen | Infant and childhood growth and frailty in old age: the Helsinki Birth Cohort Study[END_REF] , determined that greater body mass index (BMI) gain in early childhood was associated with an increased risk of frailty in late life among the men of the Helsinki Birth Cohort Study [START_REF] Haapanen | Infant and childhood growth and frailty in old age: the Helsinki Birth Cohort Study[END_REF] .

However, gaps remain in our understanding of the association between frailty in late life and body mass evolution through life. Firstly, because the description of long-term processes requires repeated measurements over decades, rarely available in epidemiological studies.

Secondly, sex differences remain poorly explored in previous studies. Yet, men and women exhibit sex differences in obesity, which have been attributed to the differential influences of sex hormones on lipid metabolism and fat distribution [START_REF] Link | Genetic Basis for Sex Differences in Obesity and Lipid Metabolism[END_REF] . These differences in exposure could lead to sex-specific associations and require that analyses are performed among men and women separately.

In a cohort of 11 784 individuals followed up annually for 26 years, we aimed to assess the relationship between weight history during adulthood and frailty in late life, in men and women separately. Three different approaches were used: the first assessed the cumulative effect of excess body fat using obesity duration; the second, the persisting effect of obesity among people with a past history of obesity; and the last approach categorized individuals according to their trajectories of BMI over adult life.

Methods

Population

Our study is part of the GAZEL cohort study. Details of the study design have been described elsewhere [START_REF] Goldberg | Cohort Profile Update: The GAZEL Cohort Study[END_REF] . Briefly, in 1989, 20 625 employees (5 614 men and 3 033 (26%) women, aged between 61 and 76 years old at that time.

Frailty

Frailty was assessed in 2015 according to the five dimensions described by Fried et al [START_REF] Fried | Frailty in Older Adults: Evidence for a Phenotype[END_REF] . Except for weight loss and exhaustion, we modified the original criteria so that they could be collected by questionnaire, as previously done in other epidemiological settings [START_REF] Strandberg | Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men[END_REF] . Weight loss was defined as self-reported unintentional weight loss of ≥4.5kg or a loss of >5% of current weight during the last 12 months. Exhaustion was assessed using two questions from the Center for Epidemiological Studies Depression (CES-D) scale ("I felt that everything I did was an effort"; "I could not get going") [START_REF] Morin | Psychometric properties of the Center for Epidemiologic Studies Depression Scale (CES-D) in French clinical and nonclinical adults[END_REF] . Weakness was defined as self-reported difficulty carrying or lifting a grocery bag and/or difficulty kneeling and standing up. Mobility impairment was defined as self-reported difficulty walking 500 meters and/or difficulty climbing stairs without help.

Physical activity over a usual week was self-reported. Individuals were considered as having a low level of physical activity if they did not meet at least one of the following requirements:

(1) walk for 10 minutes or more at least 5 days a week, and (2) practice 10 minutes or more of relatively intense activity (cycling or sport) at least once a week. Frail individuals met 3 criteria or more, pre-frail 1 or 2, and non-frail 0. In cases of missing data regarding frailty criteria, individuals could still be included in the analysis if available information enabled them to be classified as pre-frail (presence of one frailty criterion and no more than one missing criterion)

or frail (presence of at least 3 frailty criteria). Otherwise, participants with missing data for whom status could not be determined with certainty were excluded.

Body mass index

BMI was assessed each year between 1990 and 2015, using self-reported weight (in kilograms)

divided by squared self-reported height (in meters). BMI categories were as follows: <18.5 kg/m², ≥18.5 and <25 kg/m², ≥25 and <30 kg/m² and ≥30 kg/m².

Other variables

Sociodemographic characteristics were sex, age, education (university, high school, or lower than high school), and marital situation (single or in a couple). Age and marital situation were updated in 2015.

Based on annual questionnaires from inception of the cohort to 2015, we defined categories of tobacco consumption (non-smoker, current smoker or former smoker) and alcohol consumption (non-or light drinker throughout the follow-up period, current moderate or heavy drinker or former moderate or heavy drinker; according to self-reported consumption of alcohol during the last week [START_REF]International Guide for Monitoring Alcohol Consumption[END_REF] ).

Health problems assessed cancer, cardiovascular disease, joint pain, psychological problems (depression, stress or anxiety) and diabetes. Cancer and cardiovascular disease were assessed using validated registers [START_REF] Chevalier | A registry of ischaemic cardiopathies among active workers at Electricité de France-Gaz de France. Program development and first results[END_REF][START_REF] Chevalier | Cancer incidence among active male workers at Electricite de France-Gaz de France[END_REF] implemented by EDF-GDF company from the beginning of the employment period and maintained after retirement. Joint pain, diabetes and psychological problems were assessed using self-reported information in the 2013, 2014, and 2015

questionnaires (at least one occurrence over the 3 years).

Statistical analysis

Population characteristics are described by numbers and percentages for categorical variables and by mean ± standard deviation and range for continuous variables.

Obesity duration was defined as the number of years where reported information indicated a BMI of ≥30 kg/m². Missing data about BMI during follow-up concerned 6 524 (55%) individuals and were imputed using the "Copy Mean" method [START_REF] Genolini | Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies[END_REF] (linear interpolation corrected for the average difference between prediction and observation in the study population) and the "Global Slope" method [START_REF] Genolini | longitudinalData: Longitudinal data, R package version 2[END_REF] (linear extrapolation of the function defined by the first and last values), for intermittent and monotone missing values respectively (further details about imputation methods in supplementary information A).

BMI trajectories were defined using latent class mixed modeling (LCMM) among respondents in 2015, in men and women separately. These models form part of growth mixture modeling, which enables different trends in longitudinal parameter changes in a population to be identified. LCMM was used to describe linear changes in BMI according to age and period of birth (before or after the end of World War II). Indeed, a previous study on the GAZEL cohort highlighted a cohort effect on weight [START_REF] Dugravot | Do socioeconomic factors shape weight and obesity trajectories over the transition from midlife to old age? Results from the French GAZEL cohort study[END_REF] , which was confirmed by exploratory data analysis.

Models defining one to five latent trajectories were compared using the Bayesian Information Criterion and the interpretability criterion derived from Nagin's criterion [START_REF] Nagin | Group-Based Trajectory Modeling in Clinical Research[END_REF] (see supplementary information B for details about models, parameters and selection).

Because diseases such as cancer and cardiovascular disease may influence weight trajectories, we conducted a sensitivity analysis with the exclusion of participants suffering from these comorbidities.

The associations between frailty and weight parameters were analyzed in four different

Results

Population

The study sample was mainly composed of men (74%) (Table 1). Mean age was 70.5 ± 2.9 years and 67.8 ± 4.2 years for men and women, respectively. Mean BMI in 2015 was 26.5 ± 3.5 kg/m² among men and 25.4 ± 4.3 kg/m² among women. Prevalence of overweight was 50% among men and 32% among women; prevalence of obesity was 15% and 14%, respectively. Women were more often frail (10%) or pre-frail (38%) than men (4% and 33%, respectively).

Obesity duration

Obesity duration was determined for 11 117 participants (94%). Among individuals with obesity in 2015, median obesity duration was 17 years for men and 16 years for women, and 76% of men and 73% of women had had obesity for 10 years or more. Those formerly with obesity accounted for 11% of the men and 7.0% of the women and were mostly individuals with overweight in 2015 (96 %). Among individuals formerly with obesity, median obesity duration was 4 years for both men and women with overweight. Mean obesity duration increased with frailty status among participants with overweight or obesity, whether men or women.

Weight trajectories

Weight trajectories were determined for the whole study sample (n=11 784). For both men and women, three clusters of BMI trajectories emerged from the data (Table 2). Figure 1 shows the weight trajectories for men (panel A) and women (panel B).

Similar patterns were observed for men and women:

-Cluster 1, comprising 95% of men and 90% of women, with high mean posterior probabilities (>95%). This group is characterized by a consistently overweight BMI for men and a consistently normal BMI for women. Mean BMI value was 25 kg/m² for men and 23 kg/m² for women at age 45, and 26 kg/m² for men and 25 kg/m² for women at age 70. Cluster 1 is referred to as "consistently normal or overweight" (reference group).

-Cluster 2, comprising 3.9% of men and 4.7% of women, with mean posterior probability greater than 80%. Cluster 2 was characterized by an evolution from overweight to obesity. Mean BMI was 29 kg/m² for men and 27 kg/m² for women at age 45, increasing to 35 kg/m² at age 70 for men and women. Cluster 2 is referred to as "increasing BMI".

-Cluster 3, comprising 1.5% of men and 5.1% of women with correct mean posterior probability. Cluster 3 was characterized by consistent obesity: mean value of BMI was 34 kg/m² for men and 32 kg/m² for women at age 45, decreasing slowly to 31 kg/m² at age 70 for men and women. Cluster 3 is referred to as "consistently high BMI".

Health differences were observed between clusters. First of all, the prevalence of frailty and pre-frailty was higher in clusters 2 and 3 compared to cluster 1 (Table 3). A higher prevalence of diabetes, joint pain and cardiovascular disease (men only) was also observed among individuals included in clusters 2 and 3. Socio-demographic and lifestyle differences were also observed, with a lower level of education of men in clusters 2 and 3. They were also more likely to be former smokers. The men and women in cluster 3 had the lowest consumption of alcohol during the follow-up.

Sensitivity analysis excluding participants suffering from cancer or cardiovascular disease led to similar clusters of BMI trajectories.

Association between body size and frailty

The cross-sectional association between frailty and obesity shows that individuals with obesity were far more likely to be pre-frail or frail compared to the reference category (aOR >8 among women) (Model 1, Table 4).

Among individuals with obesity, each additional year of obesity increased the risk of frailty with an aOR of 1.04 [1.00 -1.08] for men and 1.07 [1.02 -1.13] for women (Model 2, Table 4). The risk of pre-frailty also increased with obesity duration.

The purpose of model 3 was to assess whether history of obesity is associated with frailty status among participants without obesity in 2015. The analyses were restricted to participants with overweight, due to the low number of individuals formerly with obesity among those with a normal BMI in 2015. Among men with overweight, those formerly with obesity had increased risk of pre-frailty and frailty compared to men with overweight who had never experienced obesity. Similar results were observed among women, although borderline significant.

The last model assessed the relationships between frailty and trends in BMI over time.

Compared to the reference group, both the "increasing BMI" and the "consistently high BMI" groups were associated with an increased risk of pre-frailty and frailty, in both men and women. Of note is the fact that, among men, the risk of frailty was higher among men in the "increasing BMI" group compared to the "consistently high BMI" group.

1

Discussion

Main findings

In a cohort of 11 784 community-dwelling individuals followed up annually for 26 years, we investigated the relationship between weight history during adulthood and frailty later in life.

Results suggest that, firstly, for individuals with obesity in 2015, the longer an individual had spent with obesity, the higher their risk of frailty. Secondly, a past history of obesity seemed to increase the risk of frailty among individuals with overweight. Finally, we identified three clusters of BMI trajectories during adulthood. Trajectories that included obesity were associated with an increased risk of frailty among men and women.

Characteristics of the study sample in 2015

In this study, the prevalence of both frailty and pre-frailty in men and women is consistent with pooled estimates by Collard et al 29 : 5.2% of men and 9.6% of women are frail, and 37% of men and 39% of women are pre-frail.

Prevalence of overweight was 50% for men and 32% for women and prevalence of obesity was 15% for men and 14% for women, in line with previous studies in France [START_REF] Fourcade | L'état de Santé de La Population En France[END_REF] . However, because data about height and weight were self-reported, underestimation is expected.

Niedhammer et al [START_REF] Niedhammer | Validity of self-reported weight and height in the French GAZEL cohort[END_REF] actually reported that participants in the GAZEL cohort were likely to underestimate their weight and to overestimate their height, especially if concerned by overweight. Furthermore, misreporting may change with age, which may contribute to smooth the trajectories. Although this adds a source of uncertainty, specificities remain high: few individuals report a higher BMI category than the one they really are, which reduces the influence of misclassification error.

Strong associations between obesity and frailty in old age have already been reported [START_REF] Reinders | Body weight and body composition in old age and their relationship with frailty[END_REF] . In our study, associations between obesity and frailty were also high, within the range of other studies defining physical frailty based on reported indicators [START_REF] Stenholm | Midlife Obesity and Risk of Frailty in Old Age During a 22-Year Follow-up in Men and Women: The Mini-Finland Follow-up Survey[END_REF][START_REF] Strandberg | Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men[END_REF] .

Life-course obesity and frailty

Using life-course approaches allowed us to further analyze the relationship between excessive body fat and frailty.

Obesity duration explores the cumulative effect of exposure to excessive body fat [START_REF] De Koning | Commentary: Obesity-years--a new metric to measure health effects of obesity[END_REF] . It has been associated with the onset of disability [START_REF] Wong | The role of obesity duration on the association between obesity and risk of physical disability[END_REF] and with decline in walking speed [START_REF] Stenholm | Obesity History as a Predictor of Walking Limitation at Old Age[END_REF] and grip strength [START_REF] Stenholm | Association between Obesity History and Hand Grip Strength in Older Adults--Exploring the Roles of Inflammation and Insulin Resistance as Mediating Factors[END_REF] . Our results suggest a cumulative effect of exposure to excessive body fat, which may reflect the progressive tissue remodeling caused by obesity (affecting notably the cardiac tissue [START_REF] Mahajan | Impact of obesity on cardiac metabolism, fibrosis, and function[END_REF][START_REF] Reis | Association of the degree of adiposity and duration of obesity with measures of cardiac structure and function: The CARDIA study[END_REF] and joint compartments [START_REF] Thijssen | Obesity and osteoarthritis, more than just wear and tear: pivotal roles for inflamed adipose tissue and dyslipidaemia in obesityinduced osteoarthritis[END_REF] ) and metabolic changes [START_REF] Vergnaud | Weight fluctuations and risk for metabolic syndrome in an adult cohort[END_REF] . This observation raises additional concerns about the growing epidemic of obesity [START_REF] Kumar | Review of Childhood Obesity: From Epidemiology, Etiology, and Comorbidities to Clinical Assessment and Treatment[END_REF] , especially in children. Long-term exposure to excessive body fat may impair the health and functional capacity of aging cohorts of individuals with obesity.

The analysis considering past exposure to obesity informs on the persisting effect of obesity even when individuals have lost weight. Men with overweight and a past history of obesity had an increased risk of pre-frailty and frailty compared to men with overweight and no history of obesity. Similar associations were observed among women, although results were borderline significant, probably due to lower sample size.

However, these two approaches have limits: they assess exposure to excessive body fat with little regard to the overall dynamic of weight change over life.

This issue was addressed by identifying trajectories of BMI. Three clusters of trajectories were identified, with similar patterns among men and women. Other attempts to determine weight trajectories over adulthood have produced similar results in the literature. Using data from a 10-year follow-up among 10 872 elderly individuals (mean age 65 years at baseline), Mezuk et al. [START_REF] Mezuk | Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study[END_REF] identified three trajectories of weight comparable to ours, as well as an additional "weight loss" trajectory. Zajacova et al. [START_REF] Zajacova | Body Mass Trajectories and Mortality Among Older Adults: A Joint Growth Mixture-Discrete-Time Survival Analysis[END_REF] also observed the same three main trajectories of BMI, among adults aged 51 to 61 at baseline followed up over 16-years.

We observed that trajectories characterized by long-term or emerging obesity were associated with increased risk of frailty for men and women. Among men, those included in the increasing BMI cluster were particularly at risk of frailty. Increased risk of frailty in weight gain groups was also found in other studies [START_REF] Mezuk | Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study[END_REF][START_REF] Haapanen | Infant and childhood growth and frailty in old age: the Helsinki Birth Cohort Study[END_REF] . Selection bias, where the most robust individuals with obesity still participate in the study in 2015, could contribute to the observed difference in frailty risk between the weight gain group and the consistently high BMI group.

However, when trajectories were defined using the whole cohort sample (results not shown), no differences were found in terms of survival (26% vs 30%, p=0.43) or non-response (37% vs 30%, p-value = 0.09) between men identified in the weight gain group and those with consistently high BMI trajectories. Mechanisms involved in weight gain remain unclear. Our results indicate health and lifestyle differences between clusters that could be further explored in future research. Although these factors might have constituted confounding factors in the relationship between frailty and obesity, the associations remained after adjustment for comorbidity and tobacco and alcohol consumption.

Of note, similar results and conclusion were found using frailty status based on completed cases (data not shown).

Strengths and limitations

The main strength of this study is the assessment of BMI annually for 26 years, in more than 11 000 men and women, although self-reported height and weight was used. Despite the long follow-up, attrition and missing data were limited; on average, participants in this study returned 22±4 questionnaires out of 26. Missing data were imputed without introducing significant bias in body mass index: after introducing missing values among complete cases, we imputed them and observed that the mean differences between imputed and observed data did not exceed 0.2 kg/m². [START_REF] Genolini | Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies[END_REF] Various life-course approaches were used and provided complementary information that helped to provide a better understanding of the long-term effects of overweight and obesity.

The 26-year follow-up of the GAZEL cohort enabled us to examine current, cumulative, and persistent effects of overweight and obesity on frailty. However, we could not assess the effect of overweight and obesity in childhood and young adulthood in critical period models [START_REF] Ben-Shlomo | The last two decades of life course epidemiology, and its relevance for research on ageing[END_REF] .

Our assessment of frailty is based on the framework proposed by Fried et al. [START_REF] Fried | Frailty in Older Adults: Evidence for a Phenotype[END_REF] in 2001, which remains that most commonly used in the field of frailty research [START_REF] Buta | Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments[END_REF] . It also appeared the most relevant in relation to issues associated with weight change because of the conceptual assumptions of this model, in which sarcopenia plays a major role. However, we approximated the measure of walking speed and grip strength using self-reported information. It should be noted that recent findings suggest that such information is as reliable in predicting standard outcomes of frailty as physical measurements [START_REF] Papachristou | Ability of Self-Reported Frailty Components to Predict Incident Disability, Falls, and All-Cause Mortality: Results From a Population-Based Study of Older British Men[END_REF] .

Other limitations must be acknowledged. Firstly, body mass index is a crude measure of fat distribution, which changes over time, especially due to the decrease in lean mass with aging [START_REF] Stout | Physiological Aging: Links Among Adipose Tissue Dysfunction, Diabetes, and Frailty[END_REF] . As a result, we may have grouped people based on their BMI who had very different body compositions. Furthermore, the models we used may not be suited to identifying weight changes over short intervals.

Conclusion

Our results suggest that current and past obesity play a decisive role in frailty in late life. Taking into account life-course obesity, notably its duration, may help with stratifying the risk of frailty among aging individuals. From a clinical perspective, frailty assessment should not be neglected among individuals with obesity, nor should screening for former obesity. From a prevention perspective, weight control could be beneficial at all ages of life and reduce the risk of frailty in aging populations. Panel A: among men.

Panel B: among women.

  models assessing: (1) the cross-sectional association between frailty and BMI in 2015; (2) the association between frailty and obesity duration (continuous) among individuals with obesity in 2015; (3) the association between frailty and former obesity (binary), among individuals without obesity in 2015; and (4) the association between frailty and BMI trajectories. All the analyses used multinomial logistic regressions. Confounders introduced in models were chosen a priori, in line with the literature. Analyses were conducted separately in men and women. A p-value of <0.05 was considered statistically significant. Results are expressed in terms of odds ratio (OR) or adjusted OR (aOR), with 95% confidence intervals (CI). R statistical software (version 3.4.4) was used, the longitudinalData package (version 2.4.1) for imputation, the LCMM package (version 1.7.9) for latent class mixed models, and the VGAM package (version 1.0-5) for multinomial regression.

Figure 1 :

 1 Figure 1: Trajectories of body mass index with age in the GAZEL cohort, among men (panel A) and among women (panel B).

Table 4 :

 4 Associations between frailty status and body size through life in GAZEL participants, using multinomial models.

	Men		Women
	Pre-frailty	Frailty	Pre-frailty	Frailty

Funding:

The GAZEL Cohort Study was funded by EDF-GDF and INSERM, and received grants from the 'Cohortes Santé TGIR Program', Agence nationale de la recherche (ANR; ANR-08-BLAN-0028) and Agence française de sécurité sanitaire de l'environnement et du travail (AFSSET; EST-2008/1/35).

Disclosure:

The authors declared no conflict of interest. 13) 332 ( 12) 18 ( 13) 15 (10)

Tables & figures


Pierre Lescuyer

Denis Hochstrasser

Thierry Rabilloud
email: thierryrabilloud@cea.fr

How shall we use the proteomics toolbox for biomarker discovery?

Keywords: proteomics, biomarkers, plasma, biological fluids, cerebrospinal fluid

Biomarker discovery for clinical purposes is one of the major areas in which proteomics is used. However, despite considerable effort, the successes have been relatively scarce. In this perspective paper, we try to figure out and analyze the main causes for this limited success, and to suggest alternate strategies, which will avoid them, without eluding the foreseeable weak points of these strategies. Two major strategies are analyzed, namely the switch from body fluids to cell and tissues for the initial biomarker discovery step or, if body fluids must be analyzed, the implementation of highly selective protein selection strategies.

biomarker discovery. From the sucesses and failures described in the literature, current approaches are critically discussed, and strategies for a better use of proteomics in biomarker discovery are outlined. ______________________________________________________________________________ _ 1. Frame and Facts.

The present paper does not aim at being a review on biomarkers, as this has been already done (e.g. in Hu et al. [START_REF] Hu | Human body fluid proteome analysis[END_REF] ). It is an opinion paper aiming at provocative discussion on how proteomics is more or less suited to the discovery of new biomarkers, and how the power of proteomics can be used in improved strategies to maximize the probability of new markers discovery. This restricted frame implies in turn that the discussion on what is a biomarker is outside the scope of this paper. As a matter of facts, this issue has also been reviewed recently in the frame of proteomics studies 2 Because of their easy accessibility compared to tissue biopsies, biological fluids are targets of choice for looking for diagnostic, prognostic and even treatment follow up biomarkers. The paradigm is that the tissues, which are in contact with biological fluids, are prone to liberate protein components in the fluids, and that the disease-altered state will change either the spectrum or the amount of liberated proteins. Ideally, such proteins can be disease-associated biomarkers and, consequently, proteomic analysis of body fluids should reveal a lot of new diagnostic markers. However, there is obviously a huge gap between theory and reality. This statement is clearly illustrated when comparing the enormous amount of data generated until now by proteomics studies focusing on body fluids and the virtual absence of concrete results in term of discovery of new clinically applicable biomarkers. How can we explain this failure? The reason is probably not that the paradigm is false. Indeed, we know that a class of biomarkers of special interest is constituted of tissue leakage proteins, i.e. proteins that are liberated in fluids by diseased tissue differently than healthy tissue. This class of biomarkers traces back in the past, e.g. with the liver transaminases which increased level signs destruction of liver cells [START_REF] Amacher | Serum transaminase elevations as indicators of hepatic injury following the administration of drugs[END_REF] , but more recent examples are available, e.g. the prostate specific antigen (PSA) for prostate cancer [START_REF] Wang | Prostate antigen: a new potential marker for prostatic cancer[END_REF] or the troponin I and T for acute myocardial infarction [START_REF] Mair | Cardiac troponin T in the diagnosis of myocardial injury[END_REF][START_REF] Antman | Cardiac-specific troponin I levels to predict the risk of mortality in patients with acute coronary syndromes[END_REF] . However, such markers exist in limited numbers and in quite low concentration, and it can be hoped that a wide search for tissue leakage markers should provide some new and interesting candidates. Most likely, the path to success will rely on the separation of two discovery and validation phases.

Success & Failure.

Accordingly, a few success stories can be attributed to proteomics in this area, one of the earliest being the discovery of 14-3-3 proteins in cerebrospinal fluid (CSF) as markers of certain types of brain destruction, including Creutzfeldt-Jakob disease (CJD) 7. Although this marker has been challenged for its selectivity and specificity, it remains one of the rare successes of proteomics in the field of biomarkers discovery. However, it should be reminded first that CSF contains much less proteins than plasma, 1/200, so that the "buffering" of protein composition is much weaker in CSF. Second, CSF collects protein mostly from one organ, the central nervous system and the spinal cord, and third, the mass ratio between this organ and the fluid volume is much more favorable in CSF than in plasma. Altogether, a brain leakage is expected to liberate higher amounts of proteins in a lesser volume, in a relatively simpler fluid, and this can make the difference between failure and success. Another recent example is the discovery of stroke biomarkers in the spinal fluid in a first discovery phase and their validation in serum in a second clinical validation phase. The situation is much bleaker for plasma-derived biomarker discovery. Very important efforts have been devoted to find cancer-associated biomarkers, for results of limited interest. Most studies involving the comparison of plasmas obtained from healthy donors with plasmas obtained from cancer-diagnosed donors have shown essentially inflammationrelated proteins (e.g. haptoglobins or serum amyloid protein) as differential markers [START_REF] Ahmed | Proteomic-based identification of haptoglobin-1 precursor as a novel circulating biomarker of ovarian cancer[END_REF][START_REF] Deng | Plasma proteomic analysis of pancreatic cancer by 2-dimensional gel electrophoresis[END_REF][START_REF] Cheng | Oral cancer plasma tumor marker identified with bead-based affinity-fractionated proteomic technology[END_REF][START_REF] Le | Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions[END_REF][START_REF] Irmak | Stage-dependent increase of orosomucoid and zinc-alpha2-glycoprotein in urinary bladder cancer[END_REF] , which shows in turn that these markers are indeed not disease-specific. It then appears clearly that among the various biological fluids, we can oppose different types having potency and use widely different from one to another.

Different biological fluids.

Fluids collecting proteins from one particular organ or anatomic structure, such as CSF, articular fluid or pleural fluid, despite their relatively very high complexity similar to plasma, results in an increased probability of finding biomarkers related to the associated organ because of the organ proximity and a smaller dilution effect. The problem is that these fluids have to be collected in a somewhat invasive manner, not so different from a needle tissue biopsy in fact. However, in the case of CSF at least, the corresponding organ (brain) is otherwise most difficult to analyze by biopsies, especially on healthy control subjects. In this sense, these fluids are of interest although for only a limited spectrum of diseases. In addition, their rather limited availability disqualifies them as a source for early biomarker detection in a wide population of people with otherwise limited symptoms. A second category of body fluids is represented by excretory mediums, urine or bile. Urine especially is rather easy to collect and available in large amount, what could be of interest for proteomic applications. The problem is that, by definition, these fluids do not have a homogenous composition. Protein concentration in urine can change from milligrams to grams per liter depending on the disease state. In addition, these fluids contained various (usually high) concentrations of excreted compounds, such as salts, urea or solubilizing agents. Both aspects make them very difficult to analyze by comparative proteomics. Another difficulty with urine comes from the fact that, in addition to proteins collected from a few organs involved in its production and excretion (kidney and urinary tract, including bladder), a large part of the proteome originates from plasma (through glomerular filtration or leakage and tubular secretion or non reabsorption). The third type of body fluids corresponds to plasma/serum, which was presented as the most amenable to the disease-associated biomarker discovery process. This fluid is, indeed, easily collected, and the important point is that plasma collects proteins from each and every tissue, and should therefore be a "universal" source of biomarkers, opposite to other fluids such as urine and CSF. As a matter of facts, most protein biomarkers used to date in clinical practice are measured in plasma, and most of this paper will be devoted to this source of biomarkers.

4. Proteomic strategy. However, the conclusion that has to be driven at that point is that the current proteomics strategy used for biomarker discovery, i.e. comparing plasma protein profiles in normal versus diseased subjects, has proven its inefficiency or even failure during the discovery phase. There are many reasons for that, but one of the most obvious is the tremendous dynamic range of protein levels in plasma, ranging from below the nanogram per ml to tens of milligrams per ml, which is twelve orders of magnitude (from millimolar concentration of albumin to fentomolar concentration of tumor necrosis factor TNF). Indeed, 22 plasma proteins make 99% of the protein mass. It is therefore tempting to try to deplete these proteins to enhance the representation of minor proteins and thus to bring them above the threshold of detection by proteomics. Although it has received considerable attention and development, this approach has also failed, for a range of reasons. First of all, because of this enormous dynamic range, the removal process must be close to 100% efficient to be interesting. Even though, a perfect process removing the 22 abundant proteins (which is far from being achieved) will leave 8 to 10 orders of magnitude of protein levels in plasma, which also exceeds by far the current power of proteomics technologies that covers 4 orders of magnitude. In addition, less abundant interesting proteins may also be eliminated during the depletion process, resulting in loss of reliable information and potential artefactual discrepancies between samples. In other words, it is our opinion that this process is bound to fail in the discovery phase. This opinion is not restricted to a peculiar setup in proteomics. While gelbased proteomics is mostly used and has failed in this respect, shotgun proteomics has not performed much better [START_REF] Svetlov | Identification and preliminary validation of novel biomarkers of acute hepatic ischaemia/reperfusion injury using dual-platform proteomic/degradomic approaches[END_REF] , and peptide selection by binding technologies such as SELDI has not found tissue leakage markers in plasma, although this has been claimed in CSF [START_REF] Khwaja | Proteomic analysis of cerebrospinal fluid discriminates malignant and nonmalignant disease of the central nervous system and identifies specific protein markers[END_REF] . This is further exemplified by the fact that even the well characterized tissue leakage biomarkers have not been rediscovered by proteomics when the proteomics toolbox has been applied to the disease which they characterize (e.g. the liver transaminases in hepatitis) [START_REF] Amacher | Use of proteomic methods to identify serum biomarkers associated with rat liver toxicity or hypertrophy[END_REF] .

Definition & application of biomarker.

Beside these technical considerations, we believe that the current inability of body fluids proteomics to provide clinically useful markers further lies in a problem of definition: the expression "potential biomarker" does not have the same meaning for a proteomist, a clinical chemist or even a clinician at the bed side. The word biomarker itself is clearly defined 16,[START_REF] Wagner | Biomarkers and surrogate end points for fit-forpurpose development and regulatory evaluation of new drugs[END_REF] and its meaning should be consensual. However, this is the notion of potentiality associated with the word biomarker that is matter to discussion. Typical proteomic studies on body fluids end up with a list of tens to hundreds differentially expressed proteins. For a proteomist, they represent potential biomarkers. For a clinical chemist, until proven otherwise, this is still a list of proteins. The question is: what makes the potentiality? In clinical practice, a biomarker has to be evaluated by a number of criteria, including sensitivity, specificity, positive and negative predictive values which rely upon the prevalence of the disease of interest. Determination of these parameters involves studies on large cohorts of hundreds or thousands of patients during the clinical validation phase. Furthermore, these criteria are valid in the context of a well-defined pathological situation. The quantitative method (usually an immunoassay) used for such studies itself needs to fit to precise criteria in term of precision, accuracy or linear reportable range. This means that the proteomic study is just the beginning of the story and that proteomists should provide more than lists of differentially expressed proteins if they want to convince clinical chemists and physicians of the usefulness of their science for discovery of new biomarkers. This requirement for proteomic workflows to fulfill clinical studies criteria in order to focus the biomarker selection process on the most valuable candidates is highlighted in a recent paper [START_REF] Mischak | Clinical proteomics: a need to define the field and to begin to set adequate standards[END_REF] . The problem is that validation studies represent huge amount of work for a single protein. So what about, for tens or hundreds of proteins? The story of 14-3-3 proteins illustrates well the length and the complexity of the way between proteomic discovery and application into clinics. In the original study [START_REF] Harrington | Abnormal proteins in the cerebrospinal fluid of patients with Creutzfeldt-Jakob disease[END_REF] , hundreds of gels were ran to show that two differentially expressed spots, latter identified as 14-3-3 proteins 7 , were potential biomarkers of CJD. Then, numerous studies using specific immunoassays and involving hundreds of patients were performed to challenge the clinical utility of the 14-3-3 proteins. Interestingly, the conclusion was that despite good sensitivity (90-97%) and specificity (87-100%) [START_REF] Green | Cerebrospinal fluid brain-derived proteins in the diagnosis of Alzheimer's disease and Creutzfeldt-Jakob disease[END_REF] , the detection of 14-3-3 proteins in CSF is of limited clinical interest, notably because, due to the low prevalence of CJD, it has a poor positive predictive value [START_REF] Burkhard | CSF detection of the 14-3-3 protein in unselected patients with dementia[END_REF][START_REF] Aksamit | Cerebrospinal fluid 14-3-3 protein: variability of sporadic Creutzfeldt-Jakob disease, laboratory standards, and quantitation[END_REF] .

Then, what are the characteristics of a good biomarker? First, it should be as specific as possible for a given disease. In other words, it should have a low false positive rate. For example, many diseases have an inflammatory dimension, and it is therefore not very surprising to find a difference in the inflammation-related markers in a comparison between healthy subjects and subjects suffering from a variety of diseases. Recent studies have also described fragments of classical plasma proteins as cancer-related biomarkers, and it has been advocated that specific cleavage of these proteins by the proteases released by cancer cells to invade the surrounding tissues may explain these observations [START_REF] Bachmeier | Matrix metalloproteinases (MMPs) in breast cancer cell lines of different tumorigenicity[END_REF] . However, such proteolysis-derived markers pose a number of problems. For example, they will be difficult to diagnose by standard, antibody-based methods. In addition, their robustness is expected to be limited. As a matter of facts, matrix metallo-proteases (MMPs) have been put forward as the cancer-specific proteases responsible for these specific protein fragments. However, it should be kept in mind that some cell types (e.g. macrophages, also produce MMPs, although in lesser amounts. Thus, aging of a sample rich in macrophage-derived products (e.g. by inflammation) is very likely to produce such fragment markers. Second, and this is frequently overlooked, the biomarker should be robust. The clinical practice is in this sense a bit remote from the situation prevailing in research laboratories, and some markers, which appear relevant at the research laboratory level in very controlled sample collection and processing conditions are not amenable to testing at the bedside, where clinical practice constraints bring some variability, especially with sample processing and thus sample degradation. Then, for each disease, the marker should show a marked difference between the healthy, another disease and the diseased populations of interest, so that the disease can be specifically and selectively diagnosed from other pathological conditions. Finally, the marker should be useful for clinical usage, i.e. afford an interesting service for the clinicians compared to other investigation methods. For example, in the field of breast cancer, a biomarker which would be not diagnostic for tumors of a size smaller that the one detected by mammography is of limited interest, unless it offers a cost advantage or another added value, e.g. a prognostic value or a population survey applicability.

What to do?

This does not mean at all, however, that proteomics-based biomarker research should stop. It just means that it is useless to make it the way it has been made up to now in many laboratories and that it should be made differently. In particular, proteomic studies have to be designed to maximize the likelihood of identifying proteins having a real potential as disease-associated biomarkers and, thereby, facilitate the selection process for further costly and time-consuming clinical studies. Each proteomic study aiming at the discovery of biomarkers should then include two distinct and complementary steps: a discovery phase and a validation phase. The discovery phase corresponds to the classical proteomic analysis, involving protein fractionation, identification and quantitation using gel-and/or liquid chromatography-and MS-MS-based technologies. Concerning this primary phase, the current inefficiency of proteomics-based biomarker research indicates that we will probably have to change drastically the way we look for biomarkers. Accordingly, we would like to propose several simple rules.

A relevant question.

Firstly, the clinical question motivating the research should be carefully defined. This implies tight collaboration with clinical chemists and physicians to determine where there is a need for new biomarkers in term of diagnosis, prognosis, prevention, prediction, therapeutic selection or follow-up. A clear definition of the project and of the clinical benefit expected will allow choosing appropriate samples both for proteomic analysis in the discovery phase and for preliminary validation experiments in the clinical phase. It will also provide a clear frame for data interpretation and thus help selecting valuable candidate biomarkers. Collaboration with physicians is also mandatory to define proper sample collection procedures that are compatible with current clinical practices. In particular, problems of adherence of the medical and technical staff to the designed protocols should not be underestimated. For example, one requesting tissue samples from surgical resection should be aware of a possible delay in sample freezing within the operating room. It is crucial to control and monitor these pre-analytical variables since they can have a dramatic effect on quality and the reproducibility of the results 23 24 .

6.2. Appropriate samples in the discovery phase. Secondly, the discovery phase should be conducted on appropriate samples. Post mortem samples can even be used in this early phase. Post mortem samples have the advantage of being collectable on organs that are not available to sample collection of a living individual (e.g. brain). Moreover, the important proteolysis taking place after death maximizes the effects observed in sample collection procedures on living individuals, thereby selecting for the most robust markers (i.e. those surviving a strong proteolysis event). In the brute force way that has been used in several situations up to now, many are directly trying to find interesting differences in the fluid without prior investigation of the tissue of interest. In the example of solid tumors, some are looking at the differences in the plasma without any serious characterization of the tumor tissue compared to the normal one. However, reversal of this paradigm is likely to lead to interesting results. By paradigm reversal, it is meant that one should first compare normal tissue to diseased tissue or even better diseased tissue with two or several well separated etiologies, find differences, and then look for these differences by focused approaches (mass spectrometry -MRM or antibody -immunoassay based) in biological fluids. Otherwise, one can also look for organ specific proteins using for example the human atlas of proteins, the human peptide atlas and SwissProt-UniProt, and once they are found, try to look for them in clinical situations in which specific organ destruction is expected. This latter model is just trying to reproduce what takes place for now well-accepted biomarkers (e.g. troponin) at the proteomics scale, and traces back to the protohistory of proteomics [START_REF] Ziai | A novel human kidney-specific protein detected by two-dimensional electrophoresis: Isolation, radioimmunoassay, and immunohistochemical localization[END_REF] . The comparison between differently diseased versions of the same tissue or cell type has received brilliant recent demonstration on the search for a metastatic power associated biomarker [START_REF] Roessler | Identification of PSME3 as a novel serum tumor marker for colorectal cancer by combining two-dimensional polyacrylamide gel electrophoresis with a strictly mass spectrometry-based approach for data analysis[END_REF] . It can be argued that such a comparison has been carried out many times, with limited success. However, it must be kept in mind that most of the times, this comparison is intended to provide mechanistic clues for the disease, so that many proteins are excluded from the scope of interest. Furthermore, even though housekeeping proteins plague the analysis of cells in exactly the same way than major proteins in plasma, it should be kept in mind that the protein level dynamics in 6 to 8 orders of magnitude in cells, compared to the 10 or 12 orders of magnitude in plasma and 8 to 10 orders of magnitude in perfectly depleted plasma. Furthermore, the fact that few intracellular proteins are glycosylated enhances the separating power of protein separation in the case of cells versus plasma. All in all, this means that the technical progresses in proteomics will give us access to disease-typing proteins in cells well before the same level of performance will be reached in plasma. The price to pay for that is to replace the lack of power in protein analysis in proteomics by more clever and focused cell biology upfront, i.e. more use of animal and cellular models, and to take the bet for transposition to human disease. Even more, we can make the best possible use of the cellular or animal systems, for example by searching for differential proteins not in the complete cells or tissue, but in what is released in a suitable medium by the adequate cells or tissue explants. This is the strategy used for the so-called secretome studies. The fact that these conditioned media do contain a fair proportion of cytosolic proteins released by cell breakage is clearly a drawback when bona fide secreted proteins are to be studied 27 28 . However, it could become an advantage in a more general search for biomarkers [START_REF] Alvarez-Llamas | Characterization of the Human Visceral Adipose Tissue Secretome[END_REF] . For example, it has been shown recently that proteins present in the secretion medium at concentrations down to the low ng/ml range can be analyzed with standard proteomics tools [START_REF] Chevallet | Toward a better analysis of secreted proteins: the example of the myeloid cells secretome[END_REF] . Furthermore, "secretomes" are probably better samples than complete tissue or cell extracts, in the sense that there is in "secretome" samples a bias in favor of easily-liberated proteins and against major tissue proteins present in large complexes (e.g. cytoskeletal proteins) 6.3. Exception or clever tricks. Thirdly, since each rule has exception, proteomic analysis of body fluids could be of interest in some cases. This could include analysis of neighboring fluids of a particular organ or tissue (e.g. CSF for markers of brain damage [START_REF] Allard | PARK7 and nucleoside diphosphate kinase A as plasma markers for the early diagnosis of stroke[END_REF][START_REF] Lescuyer | Identification of post-mortem cerebrospinal fluid proteins as potential biomarkers of ischemia and neurodegeneration[END_REF] ). When carried on plasma or urine, the proteomic analysis should involve a fractionation step aiming at the selection of a subclass of proteins to carry out the biomarker search. One recent example is the restriction to glycoproteins [START_REF] Zhang | High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry[END_REF] . In this work, the search is carried out not on the complete glycoproteins, but on protein-derived glycopeptides. This is likely to be an important point for the success of the approach. As a matter of facts, glycosylation brings an enormous chemical diversity to proteins, and the separation of glycoforms spreads widely in the protein-separation space. Conversely, the glycopeptideselection approach by hydrazide trapping and glycosidase release of the peptides will convolute all the glycoforms of the same peptide in a single signal, thereby decreasing the noise introduced by the major glycoproteins. The recent proof of concept [START_REF] Zhang | Mass spectrometric detection of tissue proteins in plasma[END_REF] shows that this approach looks promising. However, it can be prone to opposite criticism. The first criticism is that the glycoforms by themselves can be biomarkers, and this information is lost by this procedure. The second and opposite criticism is that the major mass of proteins in a cell is not glycosylated, so that the most abundant tissue leakage markers are likely to escape this type of detection. As an example, neither the transaminases nor the troponin are glycosylated. This example of glycosylation is probably an example of a more generally applicable strategy for the direct search of markers in plasma, which could be defined as positive selection strategies. In this case, the strategy is to select the subgroup of N-glycosylated proteins. This positive selection strategy is conceptually opposed to negative selection strategies (e.g. depletion of major serum proteins), which are bound to fail. It can be forecast that other positive selection strategies could be of interest, e.g. those based on active site classes [START_REF] Liu | Activity-based protein profiling: the serine hydrolases[END_REF] . However, due to the tremendous complexity of plasma, efficient strategies will be limited to those showing very high selectivity on the chemical sense of the word. Broad selection on general parameters, e.g. by hydrophobicity or ion exchange, has been applied numerous times (e.g. by the SELDI approaches) and has demonstrated its poor value. It can be also forecast that the combination of target tissue selection and of chemical selection of protein subgroups can bring added value in the search for biomarkers [START_REF] Jessani | Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness[END_REF] , at the expense of the broadness of the approach. A special case in positive protein selective is represented by the equalizing beads approach 37, . This combinatorial approach is for sure able to reveal proteins present at very low levels, and this is a highly attractive feature in biomarker discovery. However, the very principle of this approach is to provide a limited binding capacity for each and every protein on one or a few bead(s) present in the combinatorial process. This implies in turn that one the binding sites present have been saturated (and this should be reached at rather low amounts) any quantitative dimension is lost. This is a major drawback in biomarker discovery, as "normal" values for biomarkers are generally not equal to zero. Thus, it is our opinion that this approach will release its full potency in biomarker discovery when a clever trick for restoring the quantitative dimension will be found. Finally, careful bibliographic and databases searching has to be performed to find information that could bring added value, such as tissue specificity or a link with a physiopathological mechanism and thus help selecting for further validation studies. Here, we would like to insist on the fact that the wish of having bioinformatics tools capable to automatically search, integrate and interpret information obtained even by others investigators in related pathologies or on the same tissue is far from being granted, if it will ever be, The lack of standardization between experimental workflows, the great diversity of data resources, the irregular quality of data provided by databases explain, at least partly, this matter of facts. So, time, patience, perseverance and critical mind are still, and probably for a while, the main required qualities for reliable information searching. On the other hand, bioinformatics tools are obviously requested to help researchers in this difficult task: for data storage and management, for database searching, for statistical analysis, for integration of clinical, experimental and external data, and to help in their interpretation. Standardization of procedures and tools in this area should probably represent a way to promote and facilitate data exchange and comparison. However, this subject is out of the scope of this article. 6.4. Samples for the validation phase. Once the discovery phase has been performed, it must be followed by a validation phase, which represents a full part of the biomarker identification workflow. The goal is to select from the list of proteins the ones with the highest potential considering the clinical problem that need to be solved. The sample of choice for such validation studies is obviously plasma/serum since this is the main sample in used in current clinical chemistry practice. Validation experiments, based on quantitative measurements, are carried out to confirm that the concentration of the candidate protein is significantly different between diseased and control state. They will provide preliminary values of sensitivity and specificity to determine whether the potential biomarker is amenable for large cohorts studies. Recently, MS-based techniques have been described allowing quantitative measurements of specific peptides that could be of interest for preliminary validations steps [START_REF] Anderson | Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins[END_REF] . However, the gold standard techniques for such quantitative measurements are immunoassays, mainly ELISA, thank to the unmatched sensitivity and specificity provided by antibodies. As previously stated for the discovery phase, samples have to be carefully chosen to answer to a precise clinical question. In particular, control samples should include pathologies related to the disease investigated. For example, in the context of CJD diagnosis, control samples should include other type of neurodegenerative dementias. It would be also of interest to investigate susceptibility of the potential biomarker to preanalytical factors, such hemolysis or temperature of storage.

Conclusion

The easy accessibility to blood should not mislead scientist in their quest for new biomarkers in diseases. Its enormous complexity hides, even for the most powerful analytical technique, the needle in the haystack. In addition, it is impossible to find a unique condition reflecting perfectly the state of the sample in vivo and assuring perfectly stable and controllable pre-analytical conditions in vitro. Consequently, the discovery phase should, most of the times, be separated from the validation phase and even be done on different type of samples. The relevant clinical questions should be raised first. The practicality of the test in a real hospital world should be addressed in the design of the validation phase and particularly in the choice of sample types. In conclusion, it is anticipated that in the race for biomarkers, the cleverest will win over the more blood-flooded researcher. Another conclusion could be "errare humanum est, sed persevare diabolicum"