Karine Ferrare

Luc P R Bidel

Abdulmomem Awwad

Patrick Poucheret

Guillaume Cazals

Françoise Lazennec

Jacqueline Azay-Milhau

Michel Tournier

Anne-Dominique Lajoix

Didier Tousch
email: didier.tousch@umontpellier.fr

Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect

Keywords: Ferrare, K Cichorium intybus L. root extract, LC-MS analysis, Chicoric acid and chlorogenic acid, Streptozotocin diabetic rats, Antidiabetic effect, L6 cells oxidative stress assay, L6 cell

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

wealth of health benefits including its tonic effects, the ability to ease digestive problems and to detoxify liver. In Indian traditional therapy, chicory was known to possess antidiabetic effect. In the traditional medicine of Bulgaria and Italy, chicory was used as hypoglycemic decoctions.

Aims of the studies

We wanted to obtain the complete chemical composition of the natural chicoric acid extract (NCRAE), a chicory root extract rich in chicoric acid, which previously showed its glucose tolerance effect in normal rats. To investigate if the whole NCRAE is required to be effective, we performed a comparative in vivo experiment on STZ diabetic rats treated either with NCRAE or a mixture composed of the two major compounds of NCRAE.

Materials and Methods

1 K. Ferrare and L.P.R. Bidel contributed equally to this study.

Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

LC-MS method has been used to analyze the exhaustive composition of NCRAE: we have determined that chicoric acid and chlorogenic acid represented 83.8% of NCRAE. So, we have prepared a solution mixture of chicoric acid and chlorogenic acid named SCCAM, in order to compare in vivo the antidiabetic effects of this last and NCRAE in streptozotocin diabetic rats. In vitro experiments were performed on L6 cell line both for glucose uptake and for the protective effect against H 2 O 2 oxidative stress. Also, we have evaluated DPPH and ORAC (Oxygen Radical Absorbance Capacity) antioxidative capacities of the two compositions.

Results

The LC-MS analysis confirmed the high abundance of chicoric acid (64.2%) in NCRAE and a second part of NCRAE is composed of caffeoylquinic acids (CQAs) at 19.6% with among them the chlorogenic acid. This result has permitted us to prepare a mixture of synthetic Lchicoric acid (70%) and synthetic chlorogenic acid (30%): the solution is designated SCCAM.

Our results showed that both NCRAE and SCCAM are able to improve a glucose tolerance in STZ diabetic rats after a subchronic administration of seven days. Alone NCRAE allows to significantly decrease the basal hyperglycemia after six days of treatment. To explain these difference of effects between NCRAE and SCCAM, we have compared their in vitro effects on the L6 muscle cell line both for the insulin sensitizing effect and for their protective action in pretreatment against H 2 O 2 . We have also compared their antioxidant capacities. In conclusion, we demonstrated that NCRAE, a natural extract of chicory (Cichorium intybus) rich in CRA and CQAs improves glucose tolerance and reduces the basal hyperglycemia in STZ diabetic rats. Type 2 diabetes (T2D) is amongst the most common metabolic diseases worldwide. It affected about 350 million people in 2011 and this number will achieve 550 million in 2030 [START_REF] Whiting | IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030[END_REF]. To treat this illness, oral hypoglycemic agents including insulin secretagogues such as sulfonylureas, insulin sensitizing agents such as metformin and intestinal alpha glucosidase inhibitors such as acarbose are currently used. To restore a normal glycemia, alternative therapeutics consisting to use natural compounds extracted from plants are in studies. In traditional medicine, the chicory (Cichorium intybus L.) was widely used to treat diabetes in India and Pakistan [START_REF] Street | Cichorium intybus: Traditional Uses, Phytochemistry, Pharmacology, and Toxicology[END_REF]. In their review of beneficial plants used in the popular traditional medicine of Italy and Bulgaria, [START_REF] Leporatti | Preliminary comparative analysis of medicinal plants used in the traditional medicine of Bulgaria and Italy[END_REF] have reported the traditional use of Cichorium intybus L. decoctions for their hypoglycemic property. The antidiabetic effect of an alcoholic chicory extract in streptozotocin-induced diabetic rats have been published by Pushparaj et al. (2007) and more recently by [START_REF] Ghamarian | Effect of chicory seed extract on glucose tolerance test (GTT) and metabolic profile in early and late stage diabetic rats[END_REF]. Often, the anti-hyperglycemic properties of the polyphenolic compounds have been described [START_REF] Cheng | Release of beta-endorphin by caffeic acid to lower plasma glucose in streptozotocin induced diabetic rats[END_REF][START_REF] Jung | Antihyperglycemic and antioxydant properties of caffeic acid in db/db mice[END_REF][START_REF] Namura | Synthesis of amide compounds of ferulic acid and their stimulatory effects on insulin secretion in vitro[END_REF][START_REF] Meng | Roles of Chlorogenic acid on Regulating Glucose and Lipids Metabolism: A review[END_REF].

In our previous works, we have demonstrated the anti-hyperglycemic properties of a chicory (Cichoryum intybus L.) root extract (natural chicoric acid extract; NCRAE) and a burdock (Arctium lappa L.) root (dried burdock root extract; DBRE) extract [START_REF] Azay-Milhau | Antihyperglycemic effect of a natural chicoric acid extract of chicory (Cichorium intybus L.): A comparative in vitro study with the effects of caffeic and ferulic acids[END_REF][START_REF] Tousch | Chemical analysis and antihyperglycemic activity of an original extract from Burdock root (Arctium lappa)[END_REF]. These two Asteraceas extracts contain a lot of caffeoyl-derivatives. The chicory extract (NCRAE), rich in chicoric acid (dicaffeoyl tartaric acid) has a in vitro ability to increase glucose uptake in L6 muscular cell line [START_REF] Tousch | Chicoric acid, a new compound able to enhance insulin release and glucose uptake[END_REF][START_REF] Azay-Milhau | Antihyperglycemic effect of a natural chicoric acid extract of chicory (Cichorium intybus L.): A comparative in vitro study with the effects of caffeic and ferulic acids[END_REF]. Moreover, in vivo experiments by an intraperitoneal glucose tolerance test on normal rats have brought evidence that NCRAE can improve glucose tolerance [START_REF] Azay-Milhau | Antihyperglycemic effect of a natural chicoric acid extract of chicory (Cichorium intybus L.): A comparative in vitro study with the effects of caffeic and ferulic acids[END_REF]. Caffeoyl derivatives, especially chicoric acid (CRA) and chlorogenic acid (CGA) are well documented in the literature for their antioxidant capacities [START_REF] Fraisse | Caffeoyl derivatives major antioxidant compounds of some wild herbs of the Asteraceae family[END_REF][START_REF] Schlernitzauer | Chicoric acid is an antioxidant molecule that stimulates AMP Kinase pathway in L6 myotubes and extends lifespan in Caenorhabditis elegans[END_REF][START_REF] Niggeweg | Engineering plants with increased levels of the antioxydant chlorogenic acid[END_REF][START_REF] Sato | In vitro and in vivo antioxidant properties of chlorogenic acid and caffeic acid[END_REF].

In the present work, we have investigated the antidiabetic capacity of NCRAE and endeavored understand the part played by the two most abundant caffeoyl compounds contained in this extract. For that, we performed initially the identification of all caffeoyl derivatives of the extract by LC-MS analysis. On the basis of the chemical characterization of NCRAE, we have prepared a composition named SCCAM (Synthetic Chicoric and Chlorogenic Acids Mixture) containing the two major compounds of NCRAE, in proportion of 70% of synthetic L-chicoric acid (CRA) and 30% of synthetic CGA. This ratio is very close of the NCRAE CRA/CQAs ratio. We performed an in vivo experiment on streptozotocin (STZ) diabetic rats treated by subchronic administrations of NCRAE or SCCAM. The results have shown that the two compositions are able to improve the glucose tolerance, but only NCRAE possesses the capacity to decrease the basal hyperglycemia in animals. To understand the differences of effect between NCRAE and SCCAM, we have compared their in vitro insulin sensitizing effects and their protective actions in pretreatment against H 2 O 2 on the L6 muscle cell line and also their antioxidant capacities. polyphenols in a . Commercial synthetic chicoric acid (CRA) and chlorogenic acid (CGA) were purchased by Sigma-Aldrich. For L6 rat muscular cell culture, Dulbecco's Modified Eagle's Medium (DMEM) and Fetal calf serum (FCS) was provided from Biochrom (Berlin, Germany). Cocktail medium, 2 mM L-glutamine,100 U/mL penicillin and 100 µg/mL streptomycin, were purchased by Sigma-Aldrich. For diabetes induction, streptozotocin (STZ) was also provided by Sigma-Aldrich. For LC-MS analysis, acetonitrile (Carlo-Erba, France) and formic acid (Merck, Germany) were of HPLC-grade quality. Water was permuted using a reverse osmosis Milli-Q system (Millipore, France). Chlorogenic acid, L-chicoric acid, tartaric acid, quercetin-3-O-glucuronide, kaempferol-3-O-glucuronide were provided by Sigma-Aldrich.

Materials and methods

Biological material and drugs

Chicory

Chemical analysis of NCRAE by LC-MS

In a first step, the extract was characterized using a Synapt G2-S high definition mass spectrometry system (Waters Corp., Milford, MA) equipped with electrospray ionization source, to characterize elemental composition of parent and fragment ions. Chromatographic separation was carried out at a flow rate of 0.4 mL.min -1 on Acquity H-Class ultrahigh performance liquid chromatography (UPLC) system (Waters Corp., Milford, MA), equipped with a Kinetex C18 100A column (100 x 2.1 mm, 2.6µm beads) from Phenomenex (France).

The mobile phase consisted of permuted water (solvent A) and acetonitrile (solvent B), both phases acidified by 0.1% (v/v) formic acid. Mass spectra were acquired in the positive and negative ionization mode with a capillary voltage of 3 kV. Tandem mass spectra were acquired in Fast-DDA (Data Directed Analysis) mode so that the two most abundant ions in full scan MS would trigger tandem mass spectrometry (MS 2 ). The TOF mass analyzer was calibrated using phosphoric acid in 1:1 (v:v) acetonitrile:H 2 O from 50 to 1500 m/z to obtain mass accuracy within 3 ppm. The Synapt parameters were optimized using a chlorogenic acid standard as follows: the sample cone was set at 20 V, the source and desolvation temperature were set at 120 and 600 °C, respectively. Each sample was processed with MassLynx (V4.1) software.

Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

In a second step, area of the absorbance peak of each hydroxycinnamic acid was integrated at 326 nm (maximal absorbance of CGA) and expressed as equivalent absorbance of the authentic L-chicoric acid standard (Sigma-Aldrich, Munich, Germany). Compounds were identified by their retention times, UV absorbance spectra, and MS fragmentation pattern and numbered in conformity with the IUPAC numbering system (IUPAC, 1976)

In vivo experiments

Experiments were performed in male Wistar rats from Charles River Laboratory (Lyon, France) and maintained on a 12h/12h light dark schedule. Institutional guidelines for animal care and use were followed and the ethical committee Languedoc-Roussillon agreed our protocol (number 101). Diabetes was obtained by intraperitoneal injection of STZ at the dose of 65 mg.kg -1 in rats weighing 300-320 g and thereafter maintained in individual metabolic cages. Seven days after diabetes induction, animals were divided in three groups ( four animals by group). The first group received a daily intraperitoneal (IP) administration of NCRAE at the dose of 15 mg.kg -1 dissolved in saline for 7 days. The second cohort of oral glucose tolerance test (OGTT, glucose 3 g.kg -1 ). Blood samples were collected from the tail vein of unfasting rats for plasma glucose determinations. For OGTT, animals were fasted for 4 h. Plasma and urinary glucose determinations were performed using the Glucose GOD-PAP kit (BIOLABO SA, France). Results are expressed as mg.dL -1 for glycemia. In addition, for a better analysis of the time course of glycemia, values were calculated in % from basal values. Glycosuria evaluations were expressed as the urinary volume per 24 h x urinary glucose concentrations (g/24 h).

Determination of total polyphenol content

Total polyphenol content was determined by using the Folin-Ciocalteu reagent [START_REF] Singleton | Colorimetry of total phenolics with phosphomolybdicphosphotungstic acid reagents[END_REF]. Samples (50 µg) was diluted in a final volume of 1 mL with 0.2N of Folin-Ciocalteu reagent in water and were incubated in the dark at ambient temperature for 30 minutes. Discoloration was estimated by spectrophotometry analysis at 665 nm. Gallic acid (GA) was used as the standard for curve calibration. The total phenol content of extracts was expressed as mg of GAE (equivalents GA) per g of dry extract.

2-Glucose uptake test on L6 cells.

L6 rat myocyte cells (LGC Promochem, France) were cultured in 12-well plates as previously described by [START_REF] Tousch | Chicoric acid, a new compound able to enhance insulin release and glucose uptake[END_REF]. Briefly, on the day of the experiment, cells were first starved during 4 h in DMEM supplemented with 0.1% BSA and then incubated during one hour in KRB supplemented with 0.1% BSA, 5 mM glucose in the absence or in the presence of 100 nM insulin. The two compounds at 50 µg.mL -1 were tested in the presence of 100 nM insulin. Cells were then gently washed and then incubated in 1 ml KRB containing 0.5 µCi [ 3 H] deoxyglucose per well. Uptake was stopped by three washings in cold PBS and cells were lysed in 0.1N NaOH. Radioactivity was measured and total protein concentration evaluated by a Bradford method [START_REF] Bradford | A dye binding assay for protein[END_REF]; results are expressed in cpm . mg protein -1 . 1 min -1 .

Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

Survival test at an H 2 O 2 oxidative stress on L6 cells.

The L6 cell culture has been managed as described above. Cells were seeded in 96-well plates (10 4 cells per well). On day 4, the culture medium was changed by an DMEM supplemented with FCS, cocktail medium and with the different extracts at the final concentration of 50 µg.mL -1 during 12 h. The next day, the cells were washed twice by 200 µL per well of Krebs-Ringer bicarbonate buffer, 0.1% BSA and then placed in the same buffer (200 µL per well) with or without H 2 O 2 at 40 µM. After three hours of incubation at 37°C in a 5% CO 2 chamber, cells were washed once by PBS and incubated 5 min in 100 µL of Trypan-blue (Sigma-Aldrich) solution diluted by 3 with PBS. The trypan-blue solution was eliminated and the cell layers were visualized by Stereo Zoom Microscope for Large Fields with 16 x zoom (Axio Zoom.V16, Zeiss) with a magnification of 40%. The images were checked in and the blue color densities were calculated using ImageJ 1.48v (Image Processing and Analysis in Java: https://imagej.nih.gov/ij/) software [START_REF] Schneider | NIH Image to ImageJ: 25 years of image analysis[END_REF]. The data are expressed as % of mortality.

Evaluation of the antioxidant capacities

We used both the DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) and the ORAC (Oxygen Radical Absorbance Capacity) tests. For DPPH test, we have used 100 nmoles of DPPH by assay with different quantities of samples. The decrease of absorbance at 517 nm were quantified on spectrophotometer as described by [START_REF] Villano | Radical scavenging ability of polyphenolic compounds towards DPPH free radical[END_REF]. The free radical scavenging activity of an extracts is defined by the IC 50 , i.e., the quantity of sample needed to obtain 50% of inhibition of the DPPH absorbance. Using quercetin standard (with an IC 50 of 20 µg.mL -1 in our conditions), we have been also expressed the results in nmole of quercetin equivalent (QE) per mg of sample (dry material).

The ORAC test w s s m d w h h Ox

™ ORA Ass y k ( bs I .) [START_REF] Huang | Development and validation of oxygen radical absorbance capacity assay for lipophilic antioxidants Version postprint Comment citer ce document[END_REF]. All assays have been performed using a final concentration of

Data analysis

Statistical analyses were performed using analysis of variance. In vitro data are expressed as mean ±SD and in vivo data as means ± SEM. Difference were considered significant at p < 0.05 (*) or p<0.01 (**) or p<0.001 (***) using the Statgraphics 18 ® software.

Results

Chemical analysis of NCRAE content (Table 1 and 2)

Using the Folin-Ciocalteu method, we have observed that NCRAE contains essentially polyphenols (1390 mg GAE/g of dry extract). LC-MS mass spectrometry analysis has allowed us to assign and characterize these compounds: the data are shown in Table 1 andTable 2. Twenty-three peaks were separated with the column and the chromatographic gradient we used. Most of the peaks showed absorbance spectra with two bands at 220-245 nm and 315-335 nm separated by a shoulder at 290-300 nm, which were characteristic of Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology. [START_REF] Brieudes | Phytochemical analysis and antioxidant potential of the phytonutrient-rich decoction of Cichorium spinosum and C. intybus[END_REF] was not found in NCRAE. Similarly, saponins such as cynaragenins also identified in C. intybus were not identified in NCRAE.

Assigment of hydroxycinnamic acid derivatives

Tartaric acid esters: fragmentation behavior was in accordance with previous reports [START_REF] Bergeron | Stabilization of caffeic acid derivatives in Echinacea purpurea L. glycerin extract[END_REF][START_REF] Schultz | Characterization of phenolic acids and flavonoids in dandelion (Taraxacum officinale Web. ex Wigg.) root and herb by high-performance liquid chromatography/electrospray ionization mass spectrometry[END_REF]. Peak 7 was positively identified as L-chicoric acid using pure standard.

P7 is the preponderant compound of the extract. Peaks 8 and 9 may correspond to its two other diastereoisomers, D-chicoric acid and meso-chicoric acid described by [START_REF] Buiarelli | Identification of hydroxycinnamic acid-tartaric acid esters in wine by HPLC-tandem mass spectrometry[END_REF] and [START_REF] Carazzone | Identification of phenolic constituents in red chicory salads (Cichorium intybus) by high-performance liquid chromatography with diode array detection and electrospray ionization tandem mass spectrometry[END_REF]. Similarly to [START_REF] Khoza | Identification of hydroxylcinnamoyl tartaric acid esters in Bidens pilosa by UPLC-tandem mass spectrometry[END_REF], we observed that the fragmentation pattern of the three isomers is tightly similar in electrospray ionization conditions. Peak 9 was attributed to the meso isomer because of its lower amount of fragment m/z 293 previously described (Llorach et al., 2008). No guarantee can be given that the 2012). In addition, peak 6 had similar absorbance spectrum to peak 8 previously described [START_REF] Schultz | Characterization of phenolic acids and flavonoids in dandelion (Taraxacum officinale Web. ex Wigg.) root and herb by high-performance liquid chromatography/electrospray ionization mass spectrometry[END_REF] for dandelion (246 nm, 300 sh, 332 nm). The minor peak P6 was consistent with caffeoyldihydroxyphenyllactoyltartaric acid. the fragment [caffeoyl-succinoylquinic acid-H] -. We observed the presence of a secondary peak at m/z 335 [caffeoylquinic acid-H 2 O-H] -in the MS 2 spectrum, which indicated that the succinoyl group is esterified at the 4-acyl chlorogenic acid [START_REF] Jaiswal | How to identify and discriminate between the methyl quinates of chlorogenic acids by liquid chromatography-tandem mass spectrometry[END_REF]. We assigned to esculin and positively identified using authentic standard. P22 is a common coumarin in Cichorium species [START_REF] Kisiel | A new coumarin glucoside ester from Cichorium intybus[END_REF]) also name cichoriin.

In summary, NCRAE is essentially composed of 72.4% of caffeoyl tartaric acids with chicoric (dicaffeoyls) acids that represent 64.2% of the extract and 19.6% of caffeoyl quinic acids (mono and diCQAs) among which 2.1% of methylferuloyl-O-caffeoylquinic acid. The rest of the NCRAE content is flavonoïds derivatives (6.6%). These results have led to the preparation of the SCCAM solution combining 70% of CRA and 30% of CGA, close to the 0.3 CQAs/CRA ratio of NCRAE.

3.2.

In vivo experiments.

Evidence of the obtaining of a severe diabetic state

Seven days after STZ injection in rats, glycemia raised from 108 ± 3 to 561 ± 21 mg. dL -1 and plasma insulin dropped from 3.78 ± 0.37 to 0.38 ± 0.04 ng.mL -1 i.e. a loss of 90%.

Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

Time course of the physiological parameters

The first consequences of the diabetes were a stop in the gain of body weight and a clear increase in food intake (p<0.01) (Fig. 1). The daily administration of NCRAE or SCCAM did not modify these evolutions (Fig. 1A,1B). After STZ injection, glycosuria appeared and raised to high values for all animals. NCRAE or SCCAM treatments induced a significant stabilization (Fig. 1C). So at the 6th day glycosurias of treated rats were 12.9 ± 0.6 and 14.3 ± 0.5 g/24 h respectively under NCRAE or SCCAM administrations versus 18.5 ± 1.3 g/24 h for control diabetic animals (p<0.01).

Effect on blood glucose levels

Time course of hyperglycemia.

In control diabetic rats, we observed a continuous hyperglycemia of about 600 mg.dL -1 (Fig. 2). NCRAE treatment induced a clear decrease in hyperglycemia from the 2 th to the 6 th day (433 ± 33 mg.dL -1 ) (p<0.01). In contrast SCCAM treatment did not significantly change the time course of hyperglycemia (Fig. 2A and2B). Thus, we only observed a reduction of hyperglycemia with NCRAE administration (-18% on the 6 th day, p<0.01) (Fig 2B).

Oral glucose tolerance test (OGTT) (Fig. 3).

The oral charge in glucose induced a clear increase in hyperglycemia in all diabetic animals. However, this increase is slight during the first 30 minutes with SCCAM. From 90 minutes a more rapid decrease in hyperglycemia is observed in both groups of treated rats. So at 180 minutes hyperglycemia was 412 ± 17 and 415 ± 17 mg.dL -1 respectively, for NCRAE and SCCAM treated animals versus 530 ± 40 mg.dL -1 for control diabetic rats (p<0.001).

3.3.

In vitro experiments (Fig. 4).

Insulin sensitizing investigations on L6 cells.

The results were reported on the Fig. 4A: As expected, insulin (100 nM) increased basal glucose uptake in L6 cells. Addition of NCRAE significantly increased glucose uptake at 50

Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

mg.mL -1 (p<0.05) which is in agreement with our previous report [START_REF] Tousch | Chicoric acid, a new compound able to enhance insulin release and glucose uptake[END_REF]. At the same concentration of 50 µg.mL -1 , SCCAM solution has also led to an increase of the glucose uptake (p<0.01) with a value that is close to the NCRAE values.

Oxidative stress (H 2 O 2 ) survival test on L6 cells.

L6 cells were submitted at a pretreatment with NCRAE or SCCAM during 12 hours after that the compounds are eliminated by two washes. Then, 40 µM H 2 O 2 oxidative treatment are applied. The mortality of the cells is quantified using a vital trypan blue dye (Fig. 4B). In these conditions, the SCCAM pretreatment did not have a significant protective effect while NCRAE pretreatment induced a low but significant protective effect with a mortality of 46.8 ± 3.6 % comparatively in the control at 55.4 ± 3.6 % (p<0.05).

Quantification of the antioxidant capacities.

NCRAE exhibited a DPPH-free radical scavenging with a IC 50 at 61.4±1.75 µg.mL -1 and a QE.mg -1 at 1077 ± 31 nmoles compared at SCCAM with a IC50 at 29.6±1.2 µg.mL -1 and a QE.mg -1 at 2240 ± 91 nmoles. The ORAC (Oxygen Radical Absorbance Capacity) test results were similar to those of DPPH tests with a TE at 3619 ± 48 nmoles.mg -1 for NCRAE and 7020 ± 69 nmoles.mg -1 for SCCAM. So, the chemical antioxidant capacity of SCCAM is clearly higher that NCRAE.

Discussion

The present study demonstrates that NCRAE, a natural caffeoyl derivatives extract, is able to reduce basal hyperglycemia and to improve oral glucose tolerance in streptozotocin diabetic rats, pointing out that NCRAE may be considered of interest for the treatment of diabetes. described in lower quantity in roots than in the aerial parts of chicory [START_REF] Al-Snafi | The contents and Pharmacological Importance of Corchorus capsularis: A Review[END_REF][START_REF] Brieudes | Phytochemical analysis and antioxidant potential of the phytonutrient-rich decoction of Cichorium spinosum and C. intybus[END_REF] shows that the chromatography procedure we used allows to concentrate the caffeoyl derivatives. The presence of quercetin-3-O-glucuronide in NCRAE was described in the roots of chicory [START_REF] Schultz | Characterization of phenolic acids and flavonoids in dandelion (Taraxacum officinale Web. ex Wigg.) root and herb by high-performance liquid chromatography/electrospray ionization mass spectrometry[END_REF]. We can also note the presence of Ferulic acid derivatives at a lower proportion (2.1%).

STZ diabetes induced rat model is known as an appropriate model to search for potential therapies since it mimics several metabolic disorders observed in human diabetes [START_REF] Urgate | Metabolomic analysis of rat serum in streptozotocin-induced diabetes and after treatment with oral triethylenetetramine (TETA)[END_REF]. In our STZ model, the increase in food intake and the absence in body weight gain are in agreement with the previous report [START_REF] Akbarzadeh | Induction of diabetes by streptozotocin in rats[END_REF]. STZ injection induced a severe diabetes, i.e., insulinemia dramatically dropped (-90%) due to the fall in the pancreatic insulin content [START_REF] Masiello | Experimental NIDDM. Development of a new model in adult rats administered streptozotocin and nicotinamide[END_REF]. In these conditions, the antidiabetic effect of NCRAE cannot be the consequence of a pancreatic insulinotropic action previously reported on INS1 cells [START_REF] Tousch | Chicoric acid, a new compound able to enhance insulin release and glucose uptake[END_REF]. One possibility to explain the antidiabetic property of NCRAE could be an insulin sensitizing effect on peripheral tissues as muscles. Indeed, we have previously demonstrated that NCRAE has the ability in vitro to enhance glucose uptake in L6 muscle cell line and does not act on hepatic glucagon-induced glycogenolysis via hepatic glucose 6 phosphatase activity [START_REF] Azay-Milhau | Antihyperglycemic effect of a natural chicoric acid extract of chicory (Cichorium intybus L.): A comparative in vitro study with the effects of caffeic and ferulic acids[END_REF]. The results on L6 muscular cells are in agreement with the report of [START_REF] Lee | CAPE (caffeic acid phenethyl ester) stimulates glucose uptake through AMPK (AMP-activated protein kinase) activation in skeletal muscle cells[END_REF] on the same in vitro model. Here, we have shown that SCCAM possess a similar capacity that NCRAE to enhance the glucose uptake on the L6 cells, indicating the value of the mixture of chicoric acid and chlorogenic acid on insulin sensitivity. The presence of Ferulic acid derivatives in NCRAE can be beneficial since trans-Ferulic acid has been reported to exert an antidiabetic effect by modulating insulin signaling pathway in the liver of type 2 diabetic rats [START_REF] Narasimhan | Ferulic acid exerts its antidiabetic effect by modulating insulin-signaling molecules in the liver of high-fat diet and fructoseinduced type-2 diabetic adult male rat[END_REF]. The delayed improvement in OGTT can be due to the insulin sensitizing action of NCRAE by caffeoyltartaric acid derivatives and the stabilization of glycosuria may be related to the decrease in basal hyperglycemia. Regarding the SCCAM treatment, it did not change basal hyperglycemia in STZ diabetic rats.

However an immediate and clear improvement in OGTT was observed. The capacity of CGA to enhance glucose uptake in peripheral tissues and to reduce hepatic glycogenolysis [START_REF] Meng | Roles of Chlorogenic acid on Regulating Glucose and Lipids Metabolism: A review[END_REF] by inhibition of glucose 6-phosphatase activity [START_REF] Hemmerle | Chlorogenic acid and synthetic chlorogenic acid derivatives: novel inhibitors of hepatic glucose-6-phosphate translocase[END_REF] can explain the stabilization in glycosuria and OGTT improvement with SCCAM treatment. Moreover, increasing evidence showed that oxidative stress participates in pathogenesis of diabetes [START_REF] Maritim | Diabetes, oxidative stress, and antioxidants: A review[END_REF][START_REF] Msolly | Hydrogen peroxide: an oxidant stress indicator in type 2 diabetes mellitus[END_REF][START_REF] Nikolic | Anti-diabetic effects of Cichorium intybus in streptozotocin-induced diabetic rats[END_REF]. So, it has been necessary to consider the antioxidant capacities of the two products. For their antioxidant potential, the two compositions revealed significant activities, around twice for SCCAM. Consequently, a direct antioxidant effect cannot be responsible of the basal glycemia decrease observed only with NCRAE. In regard to the capacity of cell protection against H 2 O 2 oxidative stress, the treatment of 12 hours with NCRAE induced a small decrease of mortality while no effect are observed with SCCAM. Moreover in our experiments conditions, the chemical antioxidant capacity does not have any correlation with the cellular protective capacity of these compositions against H 2 O 2 .

Through a better control of hyperglycemia known to induce oxidative stress [START_REF] Esposito | Inflammatory cytokine concentrations are acutely increased by hyperglycemia in humans: role of oxidative stress[END_REF][START_REF] Ceriello | Antioxidant anti-inflammatory treatment in type 2 diabetes[END_REF], NCRAE could have a protective effect on the evolution course and complications of diabetes.

In conclusion, we demonstrated that NCRAE, a natural extract rich in CRA from chicory (Cichorium intybus) improves glucose tolerance and reduces the basal hyperglycemia in STZ diabetic rats. These interesting effects of NCRAE are the result of the association of CRA and CGA but also at the presence of other hydroxycinnamic acids.

Declaration of interest

The authors declare no conflict of interest and no financial interference with the IP rights of the indigenous people. Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

Legends of Figures and Tables:

  acids 19.6 % NCRAE contained a mixture of CRA, CGA and others caffeoyl derivatives that confer this antidiabetic -NCRAE induced a antidiabetic effect, -SCCAM induced an glucose 1. Introduction

  Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  (Cichorium intybus L.) plants were collected in Montpellier (France) and identified by botanical biologists of Botanical Institute of Montpellier (Tela Botanica-Cichorium intybus L., identification number: 2121). Natural chicoric acid extract (NCRAE) of roots was obtained using the method previously described (Azay-Milhau et al., 2013) with modifications. Briefly, the powder of the part of plant was poured in a cellulose cartridge and placed in a EtOH-Water (70 : 30). The concentrated crude extract was passed through an LH20 (Sigma-Aldrich, Munich, Germany) column to recover the enriched extract Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  diabetic rats were treated by IP injections for 7 days with the SCCAM (15 mg.kg -1 ). The last group of diabetic animals received only IP saline as controls. During the treatments, we studied the time course of the following parameters: body weight, food intake, glycosuria/24 h and glycemia. At the end of the treatments, all animals were submitted to an Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  10 µg.mL -1 of both NCRAE and other compounds. I h b f 2 2'-azobis-(2methylpropionamidine) dihydrochloride (AAPH) oxidation of the antioxidant compounds led to a reduction of fluorescein fluorescence quenching measured using the Infinite 200Pro spectrofluorimeter (TECAN). Using Trolox as standard, the antioxidant activities were expressed as nmole of Trolox equivalent (TE) per mg of sample (dry material).

  hydroxycinnamic acids (P1 to P9, P12 to P16, P19 to P22). Four peaks exhibited absorbance spectra with two maximum bands at 254-256 nm and 352-354 nm (P10, P11, P17, P18) characteristic of flavonoids. Peaks P7, P10, P11 and P13 were unequivocally identified on the basis of their concordance with retention times, UV absorbance spectra, and MS 2 fragment proportions to pure authentic standards. Other peaks were tentatively assigned based on bibliographic data. Sesquiterpene lactones (three classes of bitter) previously identified in Cichorium intybus L.[START_REF] Bais | Cichorium intybus L. -cultivation, processing, utility, value addition and biotechnology, with an emphasis on current status and future prospects[END_REF][START_REF] Al-Snafi | The contents and Pharmacological Importance of Corchorus capsularis: A Review[END_REF] were completely missing in NCRAE. The 8-deacetylmatricarin-8-O-sulfate recently found in Cichorium spinosum L.

  Peaks P1, P2, P7, P8 and P9 shared the daughter ion at m/z 149.0086 corresponding to [tartaric acid-H] -fragment ion. This one gave under MS 2 fragmentation three fragments ions at m/z 131 [tartaric acid-H 2 O-H] -, at m/z 103 and at m/z 87. Peak P1 and P2 m/z 311.0407 [M-H] -yielded MS 2 fragment ions at m/z 179 [caffeic acid-H] -and m/z 135 [caffeic acid-CO 2 -H] -, and were identified as caffeoyl-O-tartaric acid isomers, also named caftaric acid isomers. Peak 2 predominantly formed the adduct m/z 623 [2 caftaric acid -H] -in MS 1 experiment, already observed by Schütz et al. (2005) for the trans isomer. Consequently, peak 1 was consistent with the cis-caftaric acid and peak 2, with the trans-caftaric acid. Peaks 7, 8, 9 exhibited absorbance spectra shapes similar to trans-chicoric acid (L-chicoric acid) with the pseudo-molecular ion m/z 473.0699 of dicaffeoyltartaric acid esters and exhibited daughter ions at m/z 311, 293, 179, 149. They corresponded to m/z 311 [caffeoyltartaric acid-H] -, 293 [caffeoyltartaric acid-H 2 O-H] -, 179 [caffeic acid-H] -, 149 [tartaric acid-H] -. This

  formation of these two later isomers was not occurring during extraction, elution or electrospray ionization processes. Peaks 8 and 9 are in much reduced amount compared to L-Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  chicoric acid. Peak P6 exhibited a parent ion m/z 491.0794, which formed in MS 2 experiment, two fragment ions at m/z 293 and m/z 329, and a fragmentation pattern already found for a tartaric acid derivative of root and herb juice of Taraxacum officinale Web. Ex Wigg.[START_REF] Schultz | Characterization of phenolic acids and flavonoids in dandelion (Taraxacum officinale Web. ex Wigg.) root and herb by high-performance liquid chromatography/electrospray ionization mass spectrometry[END_REF] and root extract of Taraxacum formosanum Kitam[START_REF] Chen | Determination of phenolic acids and flavonoids in Taraxacum formosanum Kitam by liquid chromatography-tandem mass spectrometry coupled with a post-column derivatization technique[END_REF]. Presence of fragment ion m/z 329 indicated the loss of a caffeic moiety, which was confirmed by presence of fragment ions m/z 179 [caffeic acid-H] -and m/z 135 [caffeic acid-CO 2 -H] -. MS 3 fragmentation of ion m/z 329 yielded m/z 311 [caffeoyltartaric acid-H] -, m/z 149 [tartaric acid-H] -and m/z 135 [caffeic acid-CO 2 -H] -. Presence of fragment m/z 293 was attributed to the loss of dihydroxyphenyllactic acid plus H 2 O [M-dihydroxyphenyllactic acid -H 2 O-H] -by Schütz et al. (2005) and Chen et al. (

  Caffeoylquinic acid esters : Peaks 12, 13, 14, 16 and 19 exhibited absorbance spectra shapes similar to chlorogenic acid with the daughter ion at m/z 191.0563 corresponding to [quinic acid-H] -. This one gave under MS n fragmentation two fragments ions at m/z 127 [quinic acid-CO-2H 2 O-H] -and m/z 173 [quinic acid-H 2 O-H] -. Monocaffeoylquinic acids (parent ion of m/z 353.0877) and dicaffeoylquinic acids (parent ion of m/z 515.1194) were assigned using the identification hierarchical key of quinic acid derivatives proposed by Clifford et al. (2006). Peaks 12 and 16 displayed a fragment ion at m/z 173 [quinic acid-H 2 O-H] -base peak, characteristic of the caffeoyl group esterified to the 4-OH position of quinic acid. When caffeoyl groups are esterified to the 3-OH position and 5-OH position of quinic acid, the m/z 191 [quinic acid-H] - fragment ion remains the base peak and the signal of m/z 179 [caffeic acid-H] -fragment ion is more significant for 3-OH acyled position of the quinic acid (Peak 13 and 14). The minor peak P19 had a parent ion m/z 615.1428 corresponding to C 29 H 28 O 15 and had 3 daughter ions, m/z 515 [dicaffeoylquinic acid-H] -, m/z 353 [caffeoylquinic acid-H] -, and m/z 453 suggesting Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  found that P19 correspond to 1,5-di-O-caffeoyl-3-O-succinoylquinic acid previously described in Arctium lapa L. by[START_REF] Tousch | Chemical analysis and antihyperglycemic activity of an original extract from Burdock root (Arctium lappa)[END_REF]. Peak 20 and 21 shared a pseudomolecular ion m/z 543.1508 [M-H] -corresponding to C 27 H 28 O 12 characteristic of dimethoxycinnamoyl-caffeoylquinic acids[START_REF] Alonso-Salces | Liquid chromatography coupled with ultraviolet absorbance detection electrospray ionization, collision induced dissociation and tandem mass spectrometry on a triple quadripole for the on-line characterization of polyphenols and methylxanthines in green coffee beans[END_REF], also referred as methylferuloyl-O-caffeoylquinic acid[START_REF] Jaiswal | How to identify and discriminate between the methyl quinates of chlorogenic acids by liquid chromatography-tandem mass spectrometry[END_REF]. They shared the secondary ion m/z 381.1191 [M-H] -, corresponding to the loss of the methylferuloyl moiety. This is confirmed in MS 2 experiment by the appearance of fragment ions m/z 207[dimethoxycinnamic acid-H] -and m/z 103 [dimethoxycinnamic acid-H-H 2 O-2CH 3 • -2CO] -.The absence of the secondary ion m/z 349 indicated that the second acyl group esterified on quinic acid is not a dihydroxy-methoxy-cinnamic acid residue. Therefore, according to the identification key based on the fragmentation pattern of dimethoxycinnamoyl-caffeoylquinic acids in Collision Induced Dissociation-MS-MS, established by Alonso-Salces et al. (2009), peaks 20 and 21 were tentatively assigned 3-O-methylferuloyl-4-O-caffeoylquinic acid and 3-O-methylferuloyl-5-O-caffeoylquinic acid, respectively. 3.1.2. Assigment of flavonoids and coumarins. Analysis by MS 2 fragmentation of peaks P10 and P17 resulted in a common ion at m/z 301.0353. Its MS 3 fragmentation gave 4 fragments: m/z 271 ([M-H-CH 2 O] -), m/z 255 ([M-H-H 2 O-CO] -), m/z 179 ([ 1,2 A-H] -) and ions at m/z 151 ( 1,2 A-CO), originating from a retro-Diels-Alder (RDA) reaction similarly to that from the fragmentation of a standard solution of quercetin. We observed that Peak P10 had a parent ion m/z 477.0644 [M-H] -and that its fragment ion m/z 301 corresponded to the loss of the glucuronide moiety (176 amu). In MS 2 experiment, P10 conformed to fragmentation of quercetin-3-O-glucuronide, since quercetin-4-O-glucuronide and quercetin-7-O-glucuronide have additional characteristic fragments not found in this extract (Davis et al., 2006). Therefore, peak P10 was assigned to quercetin-3-O-Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  glucuronide. Peak P17 had a parent ion m/z 505.0994 [M-H] -and yield to m/z 463 in MS 2 and the loss of 206 amu, which indicates a hexoside moiety (162 amu) linked to an acetyl group (44 amu). It could correspond to quercetin-7-O-(6''-acetyl-glucoside) since it has already been found in Cichorium (Llorach et al., 2008) and Lettuce (Ribas-Agusti et al., 2011). Analysis by MS 2 fragmentation of peaks P11 and P18 resulted in the ion at m/z 285.0435. Fragmentation of the ion at m/z 285 gave a fragment ion at m/z 255 (loss of 30 amu, [Y 0 --CH 2 OH] -) which is characteristic of kaempferol. The loss of 176 amu in MS 2 experiment by Peak P11 suggests the loss of glucuronide moiety. Peak P11 was assigned to kaempferol-7-O-rutinoside and positively confirmed by its authentic standard. Peak P18 had a pseudo-molecular ion m/z 593.1511 [M-H] -, producing secondary ions m/z 285 [kaempferol-H] -and m/z 447 [(kaempferol+hexose)-H] -, It was assigned to an undetermined kaempferol-O-hexosyl-rhamnoside. Peak P22 had a parent ion m/z 339.0650 [M-H] -corresponding to C 15 H 15 O 9 , and the daughter ions at m/z 177.0193 (C 9 H 5 O 4 ) corresponding to the loss of hexoside moiety. The MS 2 experiment yielded to m/z 133.0301 [esculetin-H] -. Peak 22 was

  The chemical analysis by LC-MS has allowed to obtain interesting information on the NCRAE composition, essentially composed of caffeoyltartaric acid isomers with L-chicoric acid (64.2%) and its other isomers and derivatives esters of caffeoylquinic acids (CQAs) (19.6%). The presence of CQAs under mono and di-caffeoylquinic acids forms has been previously described by Milala et al. (2009). The high content of chicoric acid rather Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

  Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology. Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

Figure 1 :

 1 Figure 1: Time course of body weight (A), food intake (B) and Glycosuria (C) in STZ diabetic rats before and during the daily intraperitoneal administrations of NCRAE (15 mg.Kg -1 ) or SCCAM (15 mg.Kg -1 ). Control animals received only IP saline solution. Four animals were used for each experimental group and the values are the means (±SEM). (**) p<0.01.

Figure 2 :

 2 Figure 2: Time course of hyperglycemia (mg.dL -1 ) in STZ diabetic rats during the daily intraperitoneal administrations of NCRAE (15 mg.Kg -1 ) or SCCAM (15 mg.Kg -1 ). Control animals received only IP saline solution. Four animals were used for each experimental group. Basal values were respectively 600 ± 25 mg.dL -1 , 563 ± 32 mg.dL -1 and 525±38 mg.dL -1 for control, SCCAM and NCRAE treated diabetic rats. In the box (surrounded by a black border) the glycemia is presented in % from basal values. Values are the means (±SEM) by group. (*) p<0.05 and (**) p<0.01.

Figure 3 :

 3 Figure 3: Effects of daily intraperitoneal administrations of NCRAE (15 mg.Kg -1 ) or SCCAM (15 mg.Kg -1 ) on oral glucose tolerance test (OGTT) (glucose at 3g.Kg -1 ) in STZ diabetic rats. OGTT were performed the 7 th day of treatments (n=4 rats for each experimental group) and values are the means (±SEM). (**) p<0.01 and (***) p<0.001.

Figure 4 :

 4 Figure 4: Effect of a SCCAM treatment in comparison of NCRAE on the glucose-uptake in L6 muscle cell line (A) . Values are the means (±SD) of three independent experiments. (*) p< 0.05 and (**) p<0.01. Effect of a SCCAM treatment in comparison with NCRAE on the mortality induced by H 2 O 2 oxidative stress applied on L6 muscle cell line (B). The duration of the treatment has been of 14 hours before the H 2 O 2 application at 40 µM. Values are the means (±SD) of three independent experiments. (*) p< 0.05.

Table 1 :

 1 LC-MS Fingerprint with Fragmentation of NCRAE Compounds. Peaks obtained are listed in the order of elution, with their names, molecular formulas, precursor ions, and fragmentation data. TR (min): retention time in minutes. Mol. form.: molecular formula. Theo. mass: theoretical monoisotopic mass of precursor ion [M -H] -. Δppm: mass tolerance expressed in parts per million.

  Comment citer ce document : Ferrare, K., Bidel, L., Awwad, Poucheret, Cazals, G., Lazennec, Azay-Milhau, J., Tournier, Lajoix, Tousch, D. (Auteur de correspondance) (2018). Increase in insulin sensitivity by the association of chicoric acid and chlorogenic acid contained in a natural chicoric acid extract (NCRAE) of chicory ( Cichorium intybus L.) for an antidiabetic effect. Journal of Ethnopharmacology.

Figures:

  Figures:

  Figure 1

)

  Figure 2

  Figure 3

  Figure 4

Table 2 :

 2 The concentrations of each hydroxycinnamic acid were calculated by integrating Peak areas of chromatograms at 326 nm (maximal absorbance of CGA) and are expressed as Equivalent Absorbance of the authentic L-chicoric acid standard at 10 -3 M.

	Peak RT	Abs 326	name
		(min)
	P1	10.21	0.003	cis-O-caffeoyltartaric acid
	P2	12.24	8.249	trans-O-caffeoyltartaric acid
	P3	19.83	0.294	trans-3-O-caffeoylquinic acid
	P4	21.64	0.042	trans-5-O-caffeoylquinic acid
	P5	22.59	0.035	trans-4-O-caffeoylquinic acid
	P6	23.63	0.179	caffeoyl-dihydroxy-phenyllactoyltartaric acid
	P7	35.19	47.853 (2S, 3S)-di-O-caffeoyltartaric acid
	P8	37.20	3.955	(2R, 3R)-di-O-caffeoyltartaric acid
	P9	38.10	12.407 meso-di-O-caffeoyltartaric acid
	P10 39.46	6.630	quercetin-3-O-glucuronide
	P11 40.39	0.019	kaempferol-3-O-glucuronide
	P12 40.86	1.203	trans-3,4-di-O-caffeoylquinic acid
	P13 41.80	7.790	trans-3,5-di-O-caffeoylquinic acid
	P14 43.47	3.743	cis-3,5-di-O-caffeoylquinic acid
	P15 43.6	0.0001 kaempferol-glucuronide isomer
	P16 44.36	5.162	trans-4,5-di-O-caffeoylquinic acid
	P17 47.44	0.0001 quercetin-7-O-(6''-acetyl-glucoside)
	P18 50.67	0.0001 kaempferol-rhamnosyl-hexoside
	P19 51.02	0.0001	1,5-di-O-caffeoyl-3-O-succinoylquinic acid
	P20 51.88	0.088	dimethoxycinnamoyl caffeoylquinic acid isomer
	P21 53.51	1.261	dimethoxycinnamoyl caffeoylquinic acid isomer
	P22 53.98	0.0001 esculin = cichoriin
	P23 55.12	1.087	feruloyl-tartaric acid isomer

Acknowledgments

We sincerely thank Dr. Gérard Ribes for consenting to give us precious scientific advice and his help to the redaction of this publication.


A Fischer

N C Friggens

D P Berry

P Faverdin

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions

Keywords: feed efficiency, mixed model, cattle, lactation, individual variation Implications

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Introduction

Achieving greater feed efficiency is one possible solution to improve the sustainability of animal production, as it implies using fewer or equally resources for a given or greater level of animal production respectively. Higher feed efficiency can be achieved at the animal level and particularly through selection breeding scheme based on feed efficiency or precision feeding. Feed efficiency is often represented as residual feed intake (RFI), at least in the scientific literature [START_REF] Berry | Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[END_REF]. Residual feed intake is defined as the difference between actual and expected feed intake. Expected feed intake is calculated as the sum of the estimated expenditures for the main biological functions, such as average daily gain and maintenance in growing animals, or lactation production, maintenance, and body reserves change in lactating animals [START_REF] Berry | Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[END_REF]. Each biological function has an associated efficiency for the conversion of feed energy into this function and this is referred to as a partial efficiency. The partial efficiencies of the main biological functions are usually estimated by the regression coefficients of a single population-wide least squares regression model relating actual feed intake to the corresponding values of these biological functions. In such models, RFI is represented as the residuals of the linear regression [START_REF] Dekkers | Genetic and biological aspect of residual feed intake in pigs[END_REF][START_REF] Sle | Dissection of Koch's residual feed intake: implications for selection[END_REF][START_REF] Berry | Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[END_REF]. A positive RFI means that the animal ate more than expected based on the sample population and is therefore deemed to have a poorer efficiency. Conversely, when RFI is negative, the animal is deemed to be more efficient than the average animal of the sample population.

The variability in RFI includes true differences in feed efficiency but also includes errors due to inaccuracies in the measurement of the variables in the model, as well as errors due to model fitting. The variance of RFI is therefore highly dependent on the accuracy of the tools used to measure the individual components in the model [START_REF] Robinson | Assessing the accuracy of modelling weight gain of cattle using feed efficiency data[END_REF]. The component of RFI that is attributable to true differences in feed efficiencies can be split into the individual deviations from the population average of the partial efficiencies of the different traits in the model, and also individual differences in biological functions that are not directly included in the model. Given this, it seems reasonable to explore methods for isolating, from RFI, the cow-specific variability in the partial efficiencies associated with each of the energy expenditures included in the model.

Methods to directly measure cow-specific partial efficiencies for the main biological functions, using approaches such as metabolic chambers, are not adapted for use in large populations. However, an indirect method, already documented in beef cattle [START_REF] Savietto | Towards an improved estimation of the biological components of residual feed intake in growing cattle[END_REF], dairy cows (Mehtiö et al., 2016b) and broilers [START_REF] Sle | Dissection of Koch's residual feed intake: implications for selection[END_REF], is to fit a mixed model with animal-specific regression coefficients for each of the biological traits in the model. In this approach, the cow-specific variability in partial efficiencies is no longer consigned to the residuals of the model but instead is captured by the cow-specific regression coefficients and regression intercepts. These cow-specific regression coefficients can then be used to calculate the cow-specific expected net energy intake (NEI) as a deviation from the population mean expected NEI [START_REF] Sle | Dissection of Koch's residual feed intake: implications for selection[END_REF].

The objective of present study was to isolate the cowspecific variability in residual energy intake (REI) in dairy cows. Two models were fitted: a multiple linear regression of lactation average NEI on the lactation averages of milk energy, metabolic BW, body condition score loss and gain to estimate the reference REI and a mixed model fitting fixed linear regressions and random cow levels on the previously cited energy expenditures and on the intercept using fortnight repeated measures for the variables. The resulting cowspecific part of predicted NEI was compared with the reference REI using a linear regression to isolate the cow-specific part of REI. The partial efficiencies of each biological trait in the model were estimated in NEI unit and compared with each other to investigate if, for a given function, partial efficiency differences between cows were a possible explanation of the overall feed efficiency differences. To minimise the part of REI which could be due to measurement errors, the experiment was carried out in a steady environment during the whole lactation using high-throughput monitoring of feed intake and of the phenotypes associated with the independent variables in the model.

Material and methods

All data originated from an experiment specifically designed to better isolate differences in feed efficiency among lactating dairy cows. This experiment was carried out in two experimental farms with similar management practices and climatic conditions: INRA UMR PEGASE Méjusseaume (Le Rheu, France) and Chambre d'Agriculture Les Trinottières (Montreuil-sur-Loir, France). The data set consisted of 119 loose-housed Holstein cows with 60 at the Méjusseaume farm and 59 at Les Trinottières farm with, respectively, 50% and 46% primiparous cows. Data were available from calving to 238 days in milk for all cows. Calving season length was 56 days for Méjusseaume and 93 days for Les Trinottières.

Diet

The same diets were fed from calving to dry off. Diets were offered ad libitum with a target refusals level of 10%/cow. The energy and protein density of the diets were estimated using the French system (Institut National de la Recherche Agronomique (INRA), 2010: UFL are amounts of net energy for lactation with 1 UFL equating to 7.12 MJ of net energy for lactation, and PDIE are amounts of metabolisable protein). At Méjusseaume, cows were all fed the same diet based on maize silage, energy concentrate, soybean cake, dehydrated alfalfa and minerals. This diet had a net energy density of 0.93 UFL/kg dry matter (DM) and a metabolisable protein concentration of 94.3 g of PDIE/kg DM. At Les Trinottières, the 59 cows were split in two groups (30 and 29 cows) such that the first group had a diet balanced in energy and protein and the second group had a diet with a high protein density. The balanced diet had a net energy density of 0.94 UFL/kg DM and metabolisable protein concentration of 90.4 g of PDIE/kg DM; this diet consisted of maize silage, straw, rapeseed cake and minerals. The protein rich diet had a net energy density of 1 UFL/kg DM and a metabolisable protein concentration of 110.2 g of PDIE/kg DM; this diet consisted Cow-specific variability in residual energy intake of maize silage, ryegrass silage, high moisture corn, dried beet pulp, soybean cake and minerals.

Measurements

On both farms, each cow had its own individual feed trough. Feed intake was measured daily for each cow as the difference between offered and the next morning's refusal weight. A bulk sample of each ingredient in the diets was analysed for composition. Analyses for forages were undertaken on samples taken five times a week on each farm and analyses for concentrates were undertaken on a single sample taken weekly at Méjusseaume and every 2 months at Les Trinottières.

Cows were milked in the morning around 0730 h and in the afternoon around 1600 h. Milk yield was measured at each milking. Milk fat and protein content were measured twice weekly using two consecutive milkings for each measure (MilkoScan, Foss, Hillerød, Denmark). Net energy for lactation (Milk E ) was calculated according to INRA (2010) as:

Milk E = MY 0:44 + 0:0055 FCÀ40 ð Þ ð Þ + 0:0033 PCÀ31 ð Þ ð Þ ½
where Milk E represents the milk net energy in UFL/day, MY the milk yield in kg/day, FC the milk fat concentration in g/kg and PC the milk protein concentration in g/kg.

Body weight was measured after each morning milking using an automatic electronic scale. Body condition was scored on a scale from 0 for an emaciated cow to 5 for a fat cow with 0.25 unit increments [START_REF] Bazin | Grille de notation de l'état d'engraissement des vaches Pie Noires[END_REF]. Body condition score (BCS) was assessed twice monthly at Les Trinottières and monthly at Méjusseaume by three different scorers per farm. The average BCS of the 3 scorers was used in the present study. These values were then smoothed using a cubic spline, with the measurement test-days as the knots, to interpolate daily BCS. The difference between BCS of two consecutive days was defined as daily BCS loss when the difference was negative and daily BCS gain when the difference was positive.

Energy efficiencies Linear fixed regression using lactation average data. REI was calculated as the difference between actual and expected NEI on a per lactation basis. Thus, expected NEI was calculated from a single multiple linear regression model fitted to all cows simultaneously. In this model, lactation mean observed NEI was regressed on the energy expenditure traits averaged over lactation; these traits were energy for milk production, for maintenance, and the sum over the lactation of daily body reserve loss (ΔBCS -) and gain (ΔBCS + ) fitted separately in the model. Distinguishing body reserves loss and gain is important because gaining body reserves costs energetically more than the energy released when body reserves are mobilised, that is the partial efficiencies of these two processes are different [START_REF] Chilliard | Variations du contenu digestif et des réserves corporelles au cours du cycle gestationlactation[END_REF]. To account for size differences for a given BCS loss or BCS gain change, ΔBCS + and ΔBCS -were both multiplied by lactation mean BW. The multiple linear regression to estimate REI was carried out using the lm function in R (R Core Team, 2016) with the following model. The independent variables were centred before regression modelling:

NEI j = μ + a Milk E j + b BW 0:75 À Á j + c BW j ΔBCS + j + d BW j ΔBCS À j + REI j ð1Þ
where NEI j is the mean NEI over the lactation for cow j (j = 1 to 119 cows), μ is the intercept, Milk Ej the mean Milk E over the lactation for cow j, (BW 0.75 ) j the lactation mean metabolic BW for cow j, BW j x ΔBCS + j the sum of daily positive BCS change over the lactation adjusted for BW for cow j, and BW j x ΔBCS - j the sum of daily negative BCS change over the lactation adjusted for BW for cow j, REI j is the residual for cow j; a, b, c, d are the model estimated coefficients.

One-step mixed model analysis using fortnightly period data. In a model, the estimated coefficient gives the efficiency of conversion of NEI into the corresponding biological trait (i.e. the partial efficiency). By definition, the individual variability in efficiency can thus be isolated using cow-specific regression coefficients on each biological trait in the model (Figure 1). The cow-specific coefficients for the biological traits were extracted from a one-step mixed model using a random intercept term and random cow effect on the coefficient of each biological trait in the model. The mixed model used fortnightly averages of NEI regressed on fortnightly averages of Milk E , metabolic BW, and the fortnightly sum of daily BCS change, ΔBCS. Body reserves change were no more split into gain and loss such as for model (1) because on a fortnight level, the effect of loss as fixed effect in the mixed model was not significant (P = 0.65). The mixed model was fitted using the PROC MIXED procedure in SAS 9.1 with a standard variance components structure for the random cow effect and a heterogeneous first order autoregressive structure for the time variable in REPEATED statement of PROC MIXED. Before model development, the potential variables were all centred. All possible random effects combinations were tested, that is, eight models: from the model including only intercept as random effect (model 2.1) to the model including random intercept as well as random coefficients for Milk E , metabolic BW and BW × ΔBCS (model 2.8):

NEI ij = μ + μ j + a Milk E ij + b BW 0:75 À Á ij + c BW ij ΔBCS ij À Á |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl} = predicted NEI ij + ε ij (2.1) NEI ij = μ + μ j + a + a j À Á Milk E ij + b + b j À Á BW 0:75 À Á ij + c + c j À Á BW ij ΔBCS ij À Á |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} = predicted NEI ij + ε ij (2.8)
where μ is the intercept for the population average, a, b, c, are the population averages for the regression coefficients of Milk E , BW 0.75 , BW × ΔBCS and μ j the cow-specific intercept for cow j (j = 1 to 119). a j , b j , c j , are the cow-specific Fischer, Friggens, Berry and Faverdin coefficients for cow j (j = 1 to 119), respectively for: Milk E , BW 0.75 and BW × ΔBCS and ε ij the residual for cow j in fortnight i.

The cow-specific partial efficiencies were defined as the random coefficients of a variable. To make the partial efficiencies comparable to each other, they were expressed in NEI unit by multiplying the random coefficient by the average of the associated variable. For example, using model 2.8, the cow-specific partial efficiency of Milk E for cow j would be calculated as follows:

Partial efficiency Milk E for cow j =a j

P 17 i=1 Milk E ij 17
These cow-specific partial efficiencies of Milk E , metabolic BW and the cow-specific intercept were compared with each other and to REI from model 1, estimating either the asymptotic correlations with the ASYCOR option in PROC MIXED when comparing random coefficients of the same model or the Pearson correlations.

Model (2.8) can be rearranged to partition the predicted NEI (identified above) into the part predicted using the population mean partial efficiencies, and the part predicted using the cow-specific deviation in the partial efficiencies, referred to hereafter as the cow-specific predicted NEI, as follows:

NEI ij = μ + a Milk E ij + b BW 0:75 À Á ij + c BW ij ΔBCS ij |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} = population mean predicted component of NEI ij + μ j + a j Milk E ij + b j BW 0:75 À Á ij + c j BW ij ΔBCS ij À Á |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} = cowÀspecific predicted NEI ij + ε ij
where μ is the intercept for the population average, a, b, c are the population averages for the regression coefficients of Milk E , BW 0.75 , BW × ΔBCS and μ j the cow-specific intercept for cow j (j= 1 to 119). a j , b j , c j , are the cow-specific coefficients for cow j (j = 1 to 119), respectively for: Milk E , BW 0.75 and BW × ΔBCS and ε ij the residual for cow j in fortnight i. The cow-specific coefficients were assumed to be independent between cows and normally distributed with a mean of 0. To isolate the part of REI which is associated with cow-specific variability, the cow-specific predicted NEI estimated with the random regressions were averaged for each cow over the 17 lactation fortnights and for each fitted mixed model. These cow-specific predicted NEI were then linearly regressed on REI. The relative magnitude of variance in REI and cow-specific traits was expressed as SD/mean NEI, hereafter referred to as CV approx (classical CV could not be calculated as the traits are centred on 0).

Results

Variable description and lactation model Metabolic BW and NEI were the least variable of all traits, as represented by their low CV (Table 1). All traits were more variable when calculated on a fortnightly basis within cow than between cows, especially body reserve changes which were at least twice more variable within cow than between cows on a fortnightly basis. The whole lactation model (model 1) explained 92% of the variation in NEI with a residual SD of 0.8 UFL/day (Table 2). This residual SD represented 3.9% of mean lactation NEI. All regression coefficients in the model were significantly different from 0 with a coefficient of 0.84 for Milk E , 0.08 UFL/kg 0.75 for metabolic BW, -0.0012 UFL/kg for ΔBCS -× BW and 0.0015 UFL/kg for ΔBCS + × BW (Table 2).

Mixed models

All mixed models converged. However the variance of the random coefficient for metabolic BW was not different from zero (P = 0.37) when fitted together with both a random coefficient for Milk E and random intercept (model 2.5 in Table 3); the variance of the random coefficient for ΔBCS × BW was always null (Table 3). All mixed models had a similar Cow-specific variability in residual energy intake residual SD of 1.8 UFL/day (Table 3). The population fixed coefficients differed according to which random component was included in the mixed model (Table 3). Across all mixed models, the population mean coefficient for Milk E varied from 0.78 to 0.81 and the coefficient for variable ΔBCS × BW was about 7 10 -4 UFL/kg per unit BCS (Table 3). The population mean coefficient for metabolic BW was 0.15 UFL/kg 0.75 for the mixed model without any random effects (model 2.0 in Table 3) and increased by 0.01 UFL/kg 0.75 when any random effect was added to the model.

Comparison of the whole-lactation linear model with the mixed models For metabolic BW, the fixed regression coefficient for the mixed model was double the value of the coefficient estimated with the lactation regression model, whereas for BW × ΔBCS the fixed regression coefficient in the mixed model was half of the coefficients in the lactation regression model (Table 2). The population mean of the intercept was substantially higher in the mixed model than the intercept in the linear fixed effect model (Table 2). The average of the residuals was -0.6 UFL/day in the mixed model, which is lower and negative whereas it is zero in the linear fixed effect model. Residual energy intake from model (1) had a CV approx of 3.9%, which was 1.1 times greater than the CV approx of the cow-specific predicted NEI identified with the mixed model. The cow-specific predicted NEI extracted from the mixed models 2.1 to 2.8 in Table 3 explained between 53% and 59% of the variability of REI from model (1) (Figure 2).

Correlations between cow-specific partial efficiencies

The cow-specific partial efficiencies are here referred to as the cow-specific coefficients expressed in NEI, that is the cow-specific coefficient multiplied by the value of the associated variable. The cow-specific partial efficiencies for Milk E and for metabolic BW calculated with model 2.5 (Table 3) were not correlated with each other: the Pearson correlation was 0.08 (P = 0.41). The asymptotic correlation

À Á j + c BW j ΔBCS j + + d BW j ΔBCS j À À Á + ϵ j Model2:2 : NEI ij = μ + μ j + a + a j À Á Milk Eij + b BW 0:75 À Á ij + c BW ij ΔBCS ij À Á ϵ ij
where j refers to cow j (j = 1 to 119 Holstein cows), i refers to lactation fortnight i (i = 1 to 17), NEI is net energy intake (UFL/day), Milk E is net energy in milk (UFL/day), BW 0.75 is metabolic BW (kg 0.75 ), ΔBCS is the BCS change within a fortnight, ΔBCS -and ΔBCS + are, respectively, the BCS loss and gain within the lactation and ε is the residual, μ is the intercept, a, b, c, d are the regression coefficients and a j is the random cow coefficient for cow j. The independent variables in the models were all centred before regression modelling.

Fischer, Friggens, Berry and Faverdin between the random coefficients of Milk E and metabolic BW was also null (r = 0.01). The random intercept was positively correlated with the cow-specific partial efficiency for metabolic BW with a Pearson coefficient of correlation of 0.55 (P < 0.05) and was not correlated with the cow-specific partial efficiency for Milk E (P = 0.83). Each of this cow-specific efficiencies correlated positively with REI from model 1 with Pearson correlations of 0.63 between REI and random intercept (P < 0.05), 0.40 between REI and cowspecific partial efficiency for Milk E (P < 0.05) and 0.27 between REI and cow-specific partial efficiency for metabolic BW (P <0.05).

NEI ij = μ + μ j + a + a j À Á Milk Eij + b + b j À Á BW 0:75 À Á ij + c + c j À Á BW ij ΔBCS ij À Á + ε ij
where μ is the intercept, a, b and c are the fixed coefficients, μ j , a j , b j and c j are the deviation of the intercept and the coefficients for cow j (j = 1 to 119 Holstein cows); and ε ij is the residual for cow j and fortnight i (i = 1 to 17), NEI is the net energy intake (UFL/day), Milk E is the net energy in milk (UFL/day), and BW 0.75 is the metabolic BW (kg 0.75 ) and ΔBCS is the body condition score change within a fortnight and ε ij are the residuals expressed in UFL/day with 1 UFL = 7115 kJ of net energy. Cow-specific variability in residual energy intake

Discussion

Cow-specific variability in feed efficiency

The cow-specific variance in feed efficiency identified in our study represented only a small part of the overall variance of NEI. Considering that the cow-specific predicted NEI in the mixed model was associated with cow-specific feed efficiency, the variance of this cow-specific NEI represented between 53% and 59% of the variance of REI from the traditionally fitted model (i.e. model 1). The remaining 41% to 47% may reflect model fitting errors or measurement errors in trait values and to a lesser extent, expenditures not included in the model. Milk energy, metabolic BW and body reserves change explained a large part of NEI variability, with only 8% left in the REI. The estimated variance in cowspecific REI from model (1) thus only represented between 4% and 5% of the variance of actual NEI. It should be noted that these results were obtained using energy-dense diets.

The high measurement frequency and accuracy of measurement achieved in the present study was associated with a model having high R² and very low REI CV approx . The model R 2 and CV approx of REI were better than those previously reported for dairy cow RFI studies (Table 4) with R 2 between 59% for [START_REF] Hurley | Inter-relationships among alternative definitions of feed efficiency in grazing lactating dairy cows[END_REF] and 85% for [START_REF] Xi | Biological mechanisms related to differences in residual feed intake in dairy cows[END_REF] and CV approx between 2.9% for Xi et al. (2016) and 13.4% for Hurley et al. (2016). As shown in Table 4, REI appears to be negatively correlated with phenotypic measurement accuracy and frequency. The higher the measurement frequency of the traits included in the statistical model, the lower the REI CV approx and the higher the R 2 of the model (Table 4). Manafiazar et al. (2013) monitored the independent variables of the model once a month and had a model with R 2 of 0.68 and a RFI CV approx of 7% whereas [START_REF] Xi | Biological mechanisms related to differences in residual feed intake in dairy cows[END_REF] monitored some traits daily and some weekly and had the highest R 2 and lowest RFI CV approx among literature studies (Table 4). Moreover, the lower the accuracy and frequency of feed intake, the lower the R 2 and the higher the RFI CV approx of the model (Table 4). Thus, the variability of RFI assessed from low frequency measures of feed intake may mostly reflect measurement errors. As shown in [START_REF] Robinson | Assessing the accuracy of modelling weight gain of cattle using feed efficiency data[END_REF], high precision phenotyping reduces the part of REI which is due to phenotyping errors and increases the part of REI which is due to true variability in feed efficiency. Another possible explanation for having a model with higher R 2 and lower REI CV approx could be a relatively low genetic variability present in both herds; genetic variation is known to exist for feed efficiency [START_REF] Berry | Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[END_REF]. Because both herds have selection indexes which are in the French average of the index of the national Holstein herd, we assumed that the results were not very atypical with respect to genetic variability.

Cow-specific partial efficiencies for lactation energy output and for maintenance expenditure The cow-specific partial efficiencies for metabolic BW and Milk E were not correlated with each other. A cow which was efficient to produce milk was therefore not automatically as efficient in the maintenance function. An opposite phenomenon was documented by [START_REF] Savietto | Towards an improved estimation of the biological components of residual feed intake in growing cattle[END_REF] in growing bulls where random intercept and random coefficient for metabolic BW were negatively correlated to each other. However in [START_REF] Savietto | Towards an improved estimation of the biological components of residual feed intake in growing cattle[END_REF] the random intercept had no variance component each time a random coefficient was added. This absence of variance component for the random intercept was also observed in the current study when the independent variables had not been centred before regression modelling. Centring the independent variables appeared therefore to be essential to minimise artifactual correlations between the random intercept and the random coefficients in the mixed model.

The cow-specific partial efficiencies were, each, positively correlated with REI from model 1. The highest correlation was observed for the random intercept. The cow-specific part of REI was therefore mostly explained by the random intercept and less by the cow-specific partial efficiencies for metabolic BW or Milk E . The differences in REI between cows may therefore less be due to differences in partial efficiencies of Milk E or metabolic BW, but rather to other energy expenditures not included in the model. These other expenditures may include digestibility which varies among cows (Berry et al., 2007; Mehtiö et al., 2016a) and is thus a likely Fischer, Friggens, Berry and Faverdin contributor to differences in feed efficiency among cows (Potts et al., 2017). The other energy expenditures not included in the model could be physical activity [START_REF] Connor | Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection[END_REF] or maintenance characteristics [START_REF] Cannas | The energetic cost of maintenance in ruminants: from classical to new concepts and prediction systems[END_REF] which are not specific to the variability associated with metabolic BW. A useful next step, therefore, would be to record individual digestibility, methane emission, and body composition in terms of lipid and protein, as well as digestive tract size, to characterise their relationship with cow-specific partial efficiencies estimated in the present study. These phenotypes are however, not currently easily measureable on a large population, but could be recorded individually once or twice in the lactation using, for example, indirect methods such as indigestible markers to measure individual digestibility (Mehtiö et al., 2016a).

Modelling on a lactation or fortnight timescale Model parameters estimated using biological traits calculated as lactation average were closer to literature values and had smaller residual SD than using fortnightly averages. The average of the residuals was negative in the models using fortnightly averages, which suggests that average NEI was overestimated, that is the coefficients were too high for the positive variables and too low for the negative variables. The coefficient for metabolic BW was three times greater when using fortnight periods than the standard coefficient of 0.055 UFL/kg 0.75 published in INRA (2010). Across the 17 lactation fortnights, BW, and thus metabolic BW, does not only change because of changes in the size of the cow (e.g. due to growth) but also due to body reserve change across the lactation. The capture of body reserve change in metabolic BW variability may explain the greater coefficient for metabolic BW and smaller coefficient for body reserve change.

The coefficients for body reserve change obtained with the lactation average model (i.e. model 1) were comparable to standard coefficients: 214 UFL/unit BCS gain and 171 UFL/ unit BCS loss in our study compared with 200 UFL/unit BCS gain and 150 UFL/unit BCS loss for a cow of 600 kg reported by [START_REF] Chilliard | Variations du contenu digestif et des réserves corporelles au cours du cycle gestationlactation[END_REF]. When loss and gain are not distinguished, the coefficient was similar to the ones in model 1 and REI was strongly correlated to REI from model 1 (r = 1). This coefficient was 1.7 times greater than the equivalent coefficient in the mixed model, that is when using fortnight periods. Fortnight periods may not be long enough to accurately measure changes in body reserves with BCS which is limited to fortnightly frequency. Indeed the CV of BCS change was greater on fortnight timescale than on a lactation timescale and the models including a cow-specific coefficient for BW × ΔBCS did not converge. However even when solving the convergence problem through adding fortnight as fixed effect in the mixed model, the cow-specific variability of BW × ΔBCS was systematically null. Manual BCS in the fortnight model may therefore not be accurate enough to measure BCS changes or to include cow-specific partial efficiencies on BCS changes in the mixed model. In this context, the advent of automatic measures of BCS would enable frequent, objective and accurate measures of BCS, and could thus be a potential method to measure more accurately BCS change over a short time period [START_REF] Fischer | Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows[END_REF]. Moreover the model assumes that the relationship between BCS and energy deposition is linear which is not well established. Other phenotypes that do not rely on a subjective assessment, such as BW change, have been used to track changes in body reserves across lactation (Mantysaari et al., 2012; Hurley et al., 2016). However, there are issues with using BW change alone because in addition to the lack of information of body composition, BW change is not only due to body reserve changes but also to gut fill changes. As gut fill changes are dependent on DM intake, correction is tricky because it would use DM intake to correct BW. The model would then include an independent trait which is dependent on the dependent trait of the model. Other methods to correct gut fill have been proposed [START_REF] Thorup | On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score[END_REF] but remain to be refined to account for the variability in gut fill throughout lactation. This interpretation of measurement's variability within short periods has also to be considered when analysing the correlation between the cow-specific coefficients. Indeed when period length was shortened from fortnights to weeks, the correlations between the cow-specific coefficients became weaker [START_REF] Fischer | Study of the between-cows variability of feed efficiency in dairy cows[END_REF].

Conclusion

Feed efficiency was measured by REI in lactating dairy cows, using a linear regression of lactation average NEI on the lactation averages of milk energy, metabolic BW and lactation sum of BCS loss and gain. A mixed model framework was subsequently used to add cow-specific regression intercept and regression coefficients to the standard intercept and regression coefficients of the biological traits in the model. Different combinations of cow-specific intercept and coefficients were compared. The cow-specific part of predicted NEI estimated with the mixed model was considered to better reflect true differences in feed efficiency. This cowspecific variability in feed efficiency only accounted for 4% to 5% of the variance of measured NEI. Using a mixed model appears to be a promising method to isolate the cow-specific component of the variability in REI which is key to develop a reliable breeding scheme on feed efficiency. However its feasibility on farm is limited because this method needs repeated and quality measurements. The variability of this cow-specific component estimated with the mixed model explained between 53% and 59% of the REI variability estimated with the lactation mean model.

Figure 1

 1 Figure1Decomposition of the residual energy intake (REI) for 2 dairy Holstein cows ( and ) having 17 measures for two variables X and Y, using either a linear model without a random cow effect or a mixed model with a random cow effect on the coefficient for a model predicting trait Y with one independent variable called X, with the random slope for X () and the population mean regression line ( ).

Figure 2

 2 Figure 2 The relationship between mean residual energy intake (REI) estimated over the lactation with a linear fixed regression and the cow-specific predicted net energy intake (Cowspec_predNEI) estimated as lactation mean from four different mixed models characterised by different random coefficients. The regression (n = 119 Holstein cows) is indicated by the solid line with its equation, R 2 and rSD. The line Y = X is shown as a stippled line and the mean value by a diamond.

Table 1

 1 Mean and standard deviation of net energy intake (NEI) and energy expenditure traits, either calculated across the whole lactation (lact.) or in successive fortnightly periods (fort.) with CV calculated within or between cows

		Mean			CV (%)
	Traits	lact.	fort. 2	lact.	fort. within cow	fort. between cows
	NEI (UFL/day)	20.7	20.7	13.5	17.9	13.5
	Energy expenditure traits
	Milk E (UFL/day)	13.7	13.7	17.5	19.7	17.5
	Milk yield (kg/day)	31.4	31.4	16.6	19.1	16.6
	Milk fat yield (g/day)	1229	1226	20.3	22.6	20.3
	Milk protein yield (g/day)	999	997	16.3	18.4	16.2
	ΔBCS -1	1.25	0.07	75.2	186	85.7
	ΔBCS + 1	1.29	0.08	53.5	138	50.0
	ΔBCS 1	0.04	0.00	1375	na	na
	BCS	2.36	2.36	20.8	23.3	20.8
	BW 0.75 (kg 0.75 )	125	125	8.8	9.6	8.8
	Milk

E = net energy for lactation; BCS = body condition score (scale from 0 for emaciated to 5 for fat); BW 0.75 = metabolic BW; 1 UFL = 7115 kJ of net energy; ΔBCS -= BCS loss; ΔBCS + = BCS gain; ΔBCS = BCS change; na = not adapted. 1 Unit depends on timescale used: for lactation timescale BCS change was summed over whole lactation (238 days in milk) and for fortnight timescale BCS change was summed over a fortnight.

2

The fortnight averages are given before centring.

Table 2

 2 Fixed coefficients and quality of fit of the linear regression using lactation averaged variables and the random regression using fortnight averaged variables

		Intercept	Milk E		BW 0.75	BW × ΔBCS +	BW × ΔBCS -	BW × ΔBCS
	Model 1	μ	P	a	P	b	P	c	P	d	P	c	P	rSD	R 2
	1	20.7	***	0.84	***	0.08	***	0.0015	***	-0.0012	***			0.8	0.92
	2.2	21.3	***	0.81	***	0.16	***					6.5 10 -4	***	1.8	/

BSC = body condition score; 1 UFL = 7115 kJ of net energy. 1 Model 1 : NEI j =μ + a Milk Ej + b BW 0:75

Table 3

 3 Description of the mixed models: estimates of the fixed coefficients, estimates of the variance components (Var) of the random effects and their associated standard error

			Fixed coefficients			Var (S y.x )
	Models 1	μ	a	b	c	μ j	a j	b j	c j	BIC	rSD
	2.0	21.2	0.78	0.15	6.8 10 -4					6385	2.0
	2.1	21.1	0.77	0.16	7.0 10 -4	0.59 (0.16)				6371	1.9
	2.2	21.3	0.81	0.16	6.5 10 -4	0.56 (0.17)	0.080 (0.019)			6299	1.8
	2.3	21.2	0.77	0.16	7.0 10 -4	0.46 (0.16)		1.0 10 -3 (7.5 10 -4 )		6373	1.8
	2.4	21.1	0.77	0.16	7.0 10 -4	0.59 (0.16)			0	6371	1.9
	2.5	21.3	0.81	0.16	6.5 10 -4	0.53 (0.18)	0.080 (0.019)	2.3 10 -4 (6.7 10 -4 )		6304	1.8
	2.6	21.3	0.81	0.16	6.5 10 -4	0.56 (0.17)	0.080 (0.019)		0	6299	1.8
	2.7	21.2	0.77	0.16	7.0 10 -4	0.46 (0.16)		1.0 10 -3 (7.4 10 -4 )	0	6373	1.8
	2.8	21.3	0.81	0.16	6.5 10 -4	0.53 (0.18)	0.080 (0.019)	2.3 10 -4 (6.7 10 -4 )	0	6308	1.8

BIC = Bayesian information criterion. 1 All models are derived from model 2.8:

Table 4

 4 Comparison of publications in dairy cows for CV (CV approx ) of residual energy intake (REI) or residual feed intake (RFI), the coefficient of determination (R 2 ) of the model, and the measurement frequency of intake and the model traits

		Intake measure		Model traits measure frequency
	References	Frequency Type	BW	Milk comp. BCS	REI or RFI CV approx (%) Diet	R 2
	Xi et al. (2016)	1/day	Direct	1/week	1/w	Not used	2.8	TMR	0.85
	Yao et al. (2013)	1/day	Direct	1/week	1/w	Not used	6.0	TMR	0.84
	Manafiazar et al. (2013) 1/day	Direct	1/month	1 /m	Not used	6.9	TMR	0.68
	Connor et al. (2013)	1/day	Direct	1/f	1/w	Not used	7.5	TMR	0.72
	Mantysaari et al. (2012) 1/day	Direct	1/week or 1/month 1/w -1 /m Not used	9.4	TMR	0.90
	Hurley et al. (2016)	4.5/lact.	Indirect 1/week	1/w	1/f	13.4	pasture 0.59
	Current study	1/day	Direct	1/day	2/w	1/month-1/f	3.8	TMR	0.93
	Current study	1/day	Direct	1/day	2/w	1 /month-1/f	3.4	TMR	na

f = fortnight; m = month; lact. = lactation; MY = milk yield; Milk comp. = milk composition in fat, protein, lactose; BCS = body condition score; na: not assessed; ni: not informed; TMR: total mixed ration; MR + C = mixed ration + concentrates; CV approx = CV calculated as the SD of RFI or REI divided by the average feed intake.

Acknowledgements

The authors warmly thank all technical staff at the INRA Méjusseaume farm and Les Trinottières (Chambre d'agriculture 49) farm who helped managing and monitoring this long-term experimentation. This work was funded by the national project Deffilait (ANR-15-CE20-0014) and APIS-GENE.

Cow-specific variability in residual energy intake