Vasileios Mavromatis
email: vasileios.mavromatis@get.omp.eu

Anatoly S Prokushkin

Mikhail A Korets

Jérôme Chmeleff

Stéphanie Mounic

Oleg S Pokrovsky

Weak impact of landscape parameters and rock lithology on Mg isotope composition of the Yenisey River and its tributaries

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

Introduction

An extensive amount of work in the past has focused on estimating elemental river fluxes into the ocean, in order to budget short and long-term elemental cycles (e.g. [START_REF] Dürr | Global spatial distribution of natural riverine silica inputs to the coastal zone[END_REF]Huh et al., 1998a;1998b;[START_REF] Huh | The fluvial geochemistry of the rivers of Eastern Siberia: III. Tributaries of the Lena and Anabar draining the basement terrain of the Siberian Craton and the Trans-Baikal Highlands[END_REF]. More recently, advances in mass spectrometry allowed for high resolution analyses of a wide number of isotopic systems, including Ca, Mg and Si. The use of the isotopic signatures of these elements in riverine transport studies has provided new insights into the processes and mechanisms of isotope fractionation during weathering but also on the elemental cycles. For example, as it has been shown earlier by [START_REF] Tipper | The magnesium isotope budget of the modem ocean: Constraints from riverine magnesium isotope ratios[END_REF] the global isotopic signature of riverine runoff exhibits a lower average δ 26 Mg value compared to that of ocean waters, allowing for a better constraint of the hydrological budget of Mg sources and sinks.

Magnesium isotopes are known to exhibit significant fractionation during weathering [START_REF] Tipper | Calcium and magnesium isotope systematics in rivers draining the Himalaya-Tibetan-Plateau region: Lithological or fractionation control? Geochim[END_REF][START_REF] Tipper | Seasonal sensitivity of weathering processes: Hints from magnesium isotopes in a glacial stream[END_REF][START_REF] Wimpenny | The behaviour of Li and Mg isotopes during primary phase dissolution and secondary mineral formation in basalt[END_REF][START_REF] Wimpenny | Investigating the behaviour of Mg isotopes during the formation of clay minerals[END_REF][START_REF] Pogge Von Strandmann | The influence of weathering processes on riverine magnesium isotopes in a basaltic terrain[END_REF][START_REF] Brenot | Magnesium isotope systematics of the lithologically varied Moselle river basin, France[END_REF]. River waters that drain silicate catchments generally appear to be enriched in the lighter Mg isotope ( 24 Mg) relative to bedrock [START_REF] Huang | Adsorption-and desorptioncontrolled magnesium isotope fractionation during extreme weathering of basalt in Hainan Island, China[END_REF][START_REF] Liu | Massive magnesium depletion and isotope fractionation in weathered basalts[END_REF][START_REF] Opfergelt | Mechanisms of magnesium isotope fractionation in volcanic soil weathering sequences, Guadeloupe[END_REF]2014;[START_REF] Wimpenny | The behaviour of Li and Mg isotopes during primary phase dissolution and secondary mineral formation in basalt[END_REF]2014;[START_REF] Tipper | Seasonal sensitivity of weathering processes: Hints from magnesium isotopes in a glacial stream[END_REF]. This has been interpreted as the uptake of the heavier Mg isotope ( 26 Mg) in Si-bearing secondary phases. Other processes that can impact the Mg isotopic composition of riverine waters are the dissolution of carbonates, secondary silicate mineral formation in soils, and dissolution of particulate suspended matter in the river channel, as well as Mg uptake by plants and its release from litterfall [START_REF] Black | Magnesium-isotope Fractionation During Plant Growth[END_REF][START_REF] Bolou-Bi | Experimental determination of magnesium isotope fractionation during higher plant growth[END_REF][START_REF] Shirokova | Using Mg Isotopes to Trace Cyanobacterially Mediated Magnesium Carbonate Precipitation in Alkaline Lakes[END_REF][START_REF] Kimmig | Biogeochemical cycling of Mg and its isotopes in a sugar maple forest in Québec[END_REF].

The motivation of this study is to verify earlier observations suggesting that large rivers are relative insensitive to lithology, but small ones exhibit a distinct difference between carbonate and silicate watersheds, especially in the case of vegetation-free watersheds [START_REF] Tipper | Seasonal sensitivity of weathering processes: Hints from magnesium isotopes in a glacial stream[END_REF]. The reasons of the uniformity met in the Mg isotope signatures of large rivers are unclear, but likely stem from 1) the  26 Mg composition of parent silicate rocks such as granites, basalts and shales that dominate in the majority of large rivers, is rather homogeneous (i.e. -0.3±0.15‰; [START_REF] Teng | Magnesium Isotope Geochemistry[END_REF] and 2) the main processes controlling Mg isotope fractionation in surface fluids are very similar, once Mg is released from the bedrocks.

These processes include i) the removal of Mg from the aqueous phase in the form of formation of-or adsorption to secondary clays and ii) the uptake of Mg by vegetation and its release from plant litter.

In this study we continue our efforts on characterizing weathering processes in major Siberian rivers [START_REF] Pokrovsky | Silicon isotope variations in Central Siberian rivers during basalt weathering in permafrost-dominated larch forests[END_REF]Mavromatis et al., 2014b;2016). Earlier we focused on the isotopic composition of major and minor tributaries of Yenisey during the spring flood.

These earlier works demonstrated that parameters such as the degree of the permafrost coverage, the type of vegetation and the lithology exhibit a rather weak influence on the Mg isotope composition of river waters [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. We have also shown that N.

Tunguska, the largest tributary of Yenisey river exhibits inter-seasonal variations in its riverine Mg isotope composition as large as 0.5‰ that can be attributed to Mg uptake from vegetation and the intensity of carbonate mineral dissolution (Mavromatis et al., 2014b). In an effort to test the effect of seasonality on the Mg isotope composition of Yenisey and its tributaries, in this study we examine the Mg isotope composition of samples collected over the course of three hydrological periods (i.e. March, May and August of 2015) and we compare them with data of June 2012 [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. These observations are coupled with radiogenic 87 Sr/ 86 Sr measurements that are used as a tracer of the bedrock lithology in the watersheds of the tributaries. The Yenisey River is the largest contributor of freshwater to the Arctic Ocean, providing 18% of the total annual river discharge [START_REF] Holmes | Climate change impacts on the hydrology and biogeochemistry of Arctic Rivers[END_REF]. Due to its highly variable lithology, forest, and wetland coverage, it is largely representative of the rivers draining to the Arctic Ocean [START_REF] Gladyshev | Differences in organic matter and bacterioplankton between sections of the largest Arctic river: Mosaic or continuum?[END_REF][START_REF] Roth | Latitude and pH driven trends in the molecular composition of DOM across a north south transect along the Yenisei River[END_REF][START_REF] Fabre | Assessment of sediment and organic carbon exports into the Arctic ocean: The case of the Yenisei River basin[END_REF]. The Yenisey river system allows for testing the mechanisms of Mg fractionation within its main channel and numerous tributaries which drain through rocks of highly variable lithology and exhibit contrasting permafrost, forest, wetland, and vegetation coverage thus allowing for distinguishing the effects of bedrock, weathering and vegetation uptake / release within this large subarctic watershed. In contrast to river systems located in tropical and the sub-tropical zones, the fresh water fluxes in boreal and permafrost-affected rivers exhibit strong seasonal variations. Actually, 45% of the annual water discharging into the Arctic Ocean occurs during the snowmelt [START_REF] Holmes | Climate change impacts on the hydrology and biogeochemistry of Arctic Rivers[END_REF], together with the largest elemental fluxes from the watersheds. In monolithological catchments in Central Siberia [START_REF] Pokrovsky | Trace element fractionation and transport in boreal rivers and soil porewaters of permafrost-dominated basaltic terrain in Central Siberia[END_REF]2013;[START_REF] Bagard | Seasonal variability of element fluxes in two Central Siberian rivers draining high latitude permafrost dominated areas[END_REF] the major source of river solutes is derived from chemical weathering of bedrock with an important contribution of element cycling through plants. The impact of the watershed's bedrock lithology (i.e. granites, basalts, carbonates or terrigenous rocks) to a river is mostly manifested during winter baseflow, when the soils are frozen and the river is fed by taliks (i.e. unfrozen water paths in continuous permafrost zone) from deep underground water reservoirs [START_REF] Bagard | Seasonal variability of element fluxes in two Central Siberian rivers draining high latitude permafrost dominated areas[END_REF]2013;[START_REF] Pokrovsky | Silicon isotope variations in Central Siberian rivers during basalt weathering in permafrost-dominated larch forests[END_REF]. Additionally, the chemical composition of river waters may be also affected by the annual biomass turnover. This effect is especially pronounced during spring freshet, when sizeable leaching of organic topsoil and plant litter can supply sizable amount of base cations including Mg to the river [START_REF] Pokrovsky | Basalt weathering in Central Siberia under permafrost conditions[END_REF]. Therefore, it can be expected that Mg isotope signatures in Arctic rivers over the year will range from a shallow organic source controlled at high flow to a deep soil source controlled at low flow. Over the winter period, the main source of Mg is expected to be deep carbonate and silicate rocks, whose dissolution may create favorable conditions for precipitation of secondary Mg-bearing minerals. During the permafrost-free period, congruent dissolution of silicate suspended material, leaching from plant litter, and uptake of Mg by vegetation in soil fluids are the dominant processes [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. The present study is designed to investigate the role of the parameters such as landscape, climate and permafrost that are significantly variant among the lithologically-contrasting small tributaries of Yenisey River across its sizeable geographical gradient. The aims are to examine the major mechanisms controlling isotopic composition in Yenisey and its tributaries over the hydrological cycle and constrain in detail the Mg budget of isotopic fluxes into the Arctic Ocean.

Sampling and analytical procedures

Sample collection

In our previous study of Mg isotopes in Siberian rivers (i.e. Mavromatis et al., 2014b) research focused on year-round survey of medium size river draining central Siberian basalts and its catchment where we studied the impact of vegetation, soil and suspended load on the Mg isotopic signature. In similar terms [START_REF] Hindshaw | Seasonal change of geochemical sources and processes in the Yenisei River: A Sr, Mg and Li isotope study[END_REF] presented recently δ 26 Mg values of Yenisey river at Igarka over the time period between April and November of 2015.

In the context of Mg isotopes in the Yenisey River and its tributaries at a single hydrological event, the end of spring flood, work has been presented by [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. Here we used higher temporal resolution for such a big riverine system, and we analyze the main stem of Yenisey River and its tributaries over 3 hydrological periods of the year: winter baseflow, peak of spring flood and summer flow.

The river samples at freshet (between 25 th and 30 th of May) and summer flow (between the 3 rd and the 9 th of August) periods of 2015 were collected during cruises on a tugboat. Winter river samples, representing winter base flow conditions, were collected under ice conditions (March and April of 2015). The samples were collected from the main Yenisey River channel and all of its significant tributaries, including the Angara, the Podkamennaya Tunguska, and the Nizhnyaya Tunguska Rivers, that together provide more than 50% of the annual Yenisey river discharge to the Arctic Ocean (Table 1, Figs. 1 and2).

During ice-free period water samples were collected in the middle of the river channels at a depth of 10-20 cm below the surface. During ice coverage in winter, water samples were collected through boreholes of ~15 cm diameter. The main stem of the Yenisey river was sampled at 9 stations. A shipboard laboratory allowed immediate sample filtration (pre-combusted Whatman glass fiber filters (GFF), 0.7 μm nominal pore size) and preservation. Note here that filter pore size has been shown to have no impact on Mg concentration (i.e. < 5%) in organic-rich river waters of the left and the main right tributaries of Yenisey [START_REF] Pokrovsky | Organic and organomineral colloids in discontinuous permafrost zone[END_REF][START_REF] Bagard | Seasonal variability of element fluxes in two Central Siberian rivers draining high latitude permafrost dominated areas[END_REF] and Mg isotopes in organic rich lacustrine waters [START_REF] Ilina | Stable (Cu, Mg) and radiogenic (Sr, Nd) isotope fractionation in colloids of boreal organic-rich waters[END_REF]. Water temperature, pH and specific conductivity were measured directly in the rivers using a portable pH/T/conductivity meter HI8734 (Hanna Instruments, USA).

Chemical and Isotopic Analyses

The concentrations of major cations in water samples were measured by ICP-MS (Agilent 7500ce) at the Observatoire Midi Pyrénées (OMP) facilities (GET laboratory, Toulouse, France). Indium and rhenium were used as internal standards to correct for instrument drift and potential matrix effects. The accuracy of the Si, Mg, Ca, and Sr analyses was assessed using the SLRS-5 river water reference material, with the difference between the certified values and the measured values to be less than 10%. In addition, Mg was analyzed by atomic absorption spectroscopy with an analytical uncertainty of 1% and a detection limit of 10 µg L -1 . The non-acidified filtered water samples were used for analyses of dissolved inorganic carbon (DIC). Elemental analyses were performed using total combustion at 800°C using a SHIMADZU Pt catalyser (TOC-VCSN) with a 5% analytical uncertainty and a detection limit of 0.1 mg L -1 (see [START_REF] Prokushkin | Sources and the flux pattern of dissolved carbon in rivers of the Yenisey basin draining the Central Siberian Plateau[END_REF]. Chloride and sulfate were determined using liquid chromatography with a DIONEX ICS-2000 system with analytical uncertainty of 2% and a detection limit of 0.02 mg L -1 .

Water samples were purified from matrix elements prior to Mg isotopic analyses by cation exchange chromatography. Prior to analyses water samples were treated with 30% H2O2 to remove organics and evaporated to dryness. Separation of the Mg from the matrix elements was done by following the protocol described by [START_REF] Mavromatis | Kinetics of Mg partition and Mg stable isotope fractionation during its incorporation in calcite[END_REF] using the AG50W-X12 resin eluted with 1.0 M HNO3. After Mg separation samples were evaporated to dryness and re-dissolved to 0.35M HNO3. The recovery of Mg processed through cationexchange chromatography was better than 99%. Impurities of about 5% were insignificant to cause isobaric interferences during mass spectrometer. Magnesium isotopic ratios were measured using a Thermo-Finnigan 'Neptune' Multi Collector Inductively Coupled Plasma Mass Spectrometer (MC-ICP-MS) at the OMP facilities (GET laboratory, Toulouse, France).

All samples were prepared in 0.35 M HNO3 and introduced into the Ar plasma with a large Stable Introduction System spray chamber (double Scott Cyclonic spray chamber).

Instrumental mass fractionation effects were corrected by sample-standard-sample bracketing, and all results are presented in delta notation relative to the DSM3 reference material as:

[δ x Mg= (( x Mg/ 24 Mg)sample / ( x Mg/ 24 Mg)DSM3 -1)×1000)] where x refers to the Mg mass of interest. All results are consistent with mass-dependent fractionation (see Table 2). The reproducibility of the δ 26 Mg analyses, assessed by replicate analyses of the mono-elemental DSM3, CAM-1 and OUMg Mg reference standards and was typically better than 0.08‰; these measurements were also in agreement with the previously published values from our lab (e.g., [START_REF] Mavromatis | Magnesium isotope fractionation during hydrous magnesium carbonate precipitation with and without cyanobacteria[END_REF], 2014a[START_REF] Beinlich | Inter-mineral Mg isotope fractionation during hydrothermal ultramafic rock alteration -Implications for the global Mg-cycle[END_REF]2018) as well as from other laboratories [START_REF] Geske | The magnesium isotope (δ26Mg) signature of dolomites[END_REF][START_REF] Riechelmann | Impact of diagenetic alteration on brachiopod shell magnesium isotope (δ 26 Mg) signatures: Experimental versus field data[END_REF]2018;[START_REF] Mavromatis | Effect of organic ligands on Mg partitioning and Mg isotope fractionation during low temperature precipitation of calcite in the absence of growth rate effects[END_REF][START_REF] Bolou-Bi | Magnesium isotope compositions of natural reference materials[END_REF][START_REF] Wombacher | Separation of Mg, Ca and Fe from geological reference materials for stable isotope ratio analyses by MC-ICP-MS and double-spike TIMS[END_REF]. The reproducibility of chromatographic separation was assessed by repeated analyses of selected samples and the total procedural process of JDo-1 (dolomite) and IAPSO seawater was better than 0.08‰ (Table 2) The 87 Sr/ 86 Sr ratios of the water samples were measured using a Triton Plus thermal ionization mass spectrometer (TIMS) at OMP facilities (GET laboratory, Toulouse, France).

Water samples were evaporated, taken up in 3M HNO3 and run through Sr-spec columns [START_REF] Jones | Riverine particulate material dissolution as a significant flux of strontium to the oceans[END_REF]2014). The purified Sr was then loaded onto outgassed Ta filaments. The samples were run at 88 Sr beam potentials of 2V and 100 ratios were collected using a multidynamic peak jumping routine. Resulting 87 Sr/ 86 Sr ratios were normalized to an 86 Sr/ 88 Sr ratio of 0.1194. Eight analyses of the SRM987 (formerly NBS987) standard yielded an average 87 Sr/ 86 Sr of 0.710215±0.000012 (2SD). The certified value of NBS987 is 0.71034 ±0.00026.

Individual analytical errors did not exceed ±0.000011 87 Sr/ 86 Sr. Total blanks for Sr were found to be negligible (<0.1%) compared to the Sr amounts from the samples.

Results

Chemical composition of Yenisey and its tributaries over the hydrological year

The chemical composition of major elements in all river water samples can be found in Table 2. Overall the maximum concentrations of Ca and Mg are lowest in May sampling (Spring period). During this sampling period, the maximum Ca concentration measured was 587 μM L -1 in Kem, a river whose bed rock lithology is dominated by sedimentary silicates (Table 1). The maximum measured Mg concentrations in samples collected during May 2015, was 189 μM L -1 in Angara, a river whose bedrock coverage includes granites and sedimentary silicates (Table 1). The concentrations of Ca and Mg over August (summer) and March (winter) campaigns are in the same order of magnitude. In detail the maximum concentration of Ca over summer and winter periods are 1382 (Kem) and 1500 (Sovinskaya) μM L -1 , respectively. Similarly, the maximum concentrations of Mg over summer and winter periods are 492 and 655 μM L -1 , respectively, and are observed in P. Tunguska a river draining through basalts, tuffs and carbonates. The contribution of major elements in the riverine water from silicate and/or carbonate sources (e.g., [START_REF] Gaillardet | Global silicate weathering and CO2 consumption rates deduced from the chemistry of large rivers[END_REF][START_REF] Gaillardet | Chemical denudation rates of the western Canadian orogenic belt: the Stikine terrane[END_REF] was assessed using Mg vs. Ca concentrations plots of riverine waters normalized against the sum of Na and K.

The obtained results exhibit a linear trend for all the three seasons (Fig. 2), in agreement with the chemical composition of Yenisey river waters collected in June of 2012 (Mavromatis et al., 2016) and a number of other Siberian rivers [START_REF] Pokrovsky | Basalt weathering in Central Siberia under permafrost conditions[END_REF].

The seasonal variations of dissolved Sr concentrations are similar to those of Mg and Ca. They ranged over an order of magnitude between spring and winter or summer, regardless of the bedrock lithology, exhibiting strong dilution in spring at high water discharge (see Table 2). For example, in the Garevka river that is a small tributary of Yenisey dominated by a granitic lithology [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF], Sr concentrations are low but different amongst seasons being equal to 0.18, 0.08 and 0.14 μM L -1 during August, May and March, respectively. Another example is P. Tunguska, which exhibited the highest Sr concentrations of all river samples collected in this study of 8.00, 0.68 and 16.21 μM L -1 during August, May and March, respectively.

Strontium isotope composition of Yenisey and its tributaries over the hydrological year

The 87 Sr/ 86 Sr ratios of Yenisey tributaries sampled during May and March campaigns plotted against the respective values of samples collected over the August campaign exhibited a linear trend (Fig. 3). The values of samples collected in August have been used here, because they reflect the maximum impact of vegetation on the chemical and isotopic composition of river waters. In addition it is worth noting that despite the large variations of Sr concentrations both amongst rivers and amongst seasons, the sample tributaries exhibit a range of 87 Sr/ 86 Sr ratios between 0.708 and 0.713. An exception is the 87 Sr/ 86 Sr ratios of Garevka and Tis, (0.728 and 0.718, respectively, Table 2). Those elevated values can likely be attributed to the predominance of granitic rocks in their watersheds. Similar values have been earlier documented for riverine water of the same tributaries over the late spring flood of 2012 [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. Overall the 87 Sr/ 86 Sr ratios of Yenisey tributaries for all the three seasonal campaigns are within the same range for Eastern Siberian rivers (Huh et al., 1998a,b;[START_REF] Huh | The fluvial geochemistry of the rivers of Eastern Siberia: III. Tributaries of the Lena and Anabar draining the basement terrain of the Siberian Craton and the Trans-Baikal Highlands[END_REF] and Yenisey coastal sediments (0.712; [START_REF] Guo | Speciation and fluxes of nutrients (N, P, Si) from the upper Yukon River[END_REF]. Within uncertainty they are also comparable to Canadian rivers (0.7111; [START_REF] Wadleigh | Strontium and its isotopes in Canadian rivers: fluxes and global implications[END_REF], and the global river average which ranges from 0.7116 [START_REF] Pearce | Reassessing the stable (δ 88/86 Sr) and radiogenic ( 87/86 Sr) strontium isotopic composition of marine inputs[END_REF] to 0.7119 [START_REF] Palmer | The strontium isotope budget of the modern ocean[END_REF].

Magnesium isotope composition of Yenisey and its tributaries over the hydrological year

The overall variation of δ 26 Mg values measured in this study spans a 0.8‰ range and falls within the range of δ 26 Mg values reported earlier for Yenisey tributaries in a post-flood period (June 2012; [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. The riverine water samples collected from the main channel of the Yenisey exhibit an increase in δ 26 Mg northwards. However samples collected in the mouth of the river exhibit variations that do not exceed 0.2‰ over the hydrological seasons. In addition, the samples collected at ~66.5N during May and August of 2015, exhibit similar values to those reported recently by [START_REF] Hindshaw | Seasonal change of geochemical sources and processes in the Yenisei River: A Sr, Mg and Li isotope study[END_REF] for the Igarka station that were also sampled in 2015 (see Fig. 4). Similarly, most tributaries did not exhibit any sizable variation of  26 Mg between seasons. Generally, there were three groups of tributaries depending on their value  26 Mgspring/summer - 26 Mgwinter: i) < 0.05‰ (Nizhnaya Tunguska, Podkamennaya Tunguska, Vorogovka, Kas); ii) 0.1 to 0.18‰ (Bolshoi Pit, Tis, Dubches, Garevka) and iii) 0.27 ‰ (Sym). We did not evidence any straightforward link between the value of this difference and the lithology, climate, size, or vegetative coverage of the river watershed. We note however that for all the tributaries plotted in Fig. 4, the Mg isotope composition is lower at the end of spring flood (2012) compared to the peak of spring flood (2015). The magnitude of this difference was not linked to lithology, size of the river, and permafrost coverage.

Moreover, the most 26 Mg enriched samples were those collected from N. Tunguska, that is draining the Central Siberian basalt province (see Fig. 4). Note here that the samples collected from N. Tunguska tributary exhibit similar seasonal variations to those reported earlier for the same river 600 km upstream its confluence with Yenisey (Mavromatis et al., 2014b). The Mg isotope composition of the water samples collected in the spring campaign of 2015 (May) of this study are systematically enriched in 26 Mg compared to samples at the end of the post-flood period in 2012 [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. Their difference range between <0.1‰ for Turukhan, to more than 0.5‰ for P. Tunguska (see Fig. 4). Note here that Turukhan drains through wetlands and peatlands of terrigenous terrain whereas P. Tunguska drains through a mixture of basalts, carbonates and terrigenous material. We attribute the observed differences to the different timing of sampling, which was at the peak of the spring flood in 2015 in this study (late May), whereas in 2012 it was the end of the spring flood (late June). Indeed, there are significant isotopic variations over the hydrological year, specially between winter, early and late spring that have been demonstrated in two rivers of Central Siberia plateau (see Mavromatis et al., 2014b) In order to test the effect of multiple factors on the Mg isotopic composition, a multivariate principle component analysis (PCA) has been conducted using the whole set of parameters that may affect isotopic fractionation for each of the three seasons individually, using the statistical software Statistica (v. 11.2). For this analysis, we considered Mg and Sr isotopic composition, major cation and anion concentration of the river water and landscape and lithological parameters of the watersheds as listed in Tables 1, 2 and S1 (see Appendix).

The PCA exhibited a rather weak capacity in explaining the variability of more than 20 parameters. The overall explanation by each of 2 factors was below 10%; the F1+F2 together explained less than 20% of overall variability. However, the PCA analysis demonstrated the following features: i) In winter, the  26 Mg value is controlled by the 2 nd factor and positively linked to Mg/Ca and Mg/(Na+K) ratios, pH and DIC (Tables S2-S5; Appendix). This can be interpreted as the effect of carbonate (notably dolomitic) rocks at the catchment, which is pronounced through underground contribution via taliks of large and medium sized rivers; ii) in spring,  26 Mg is positively linked to latitude and river suspended matter but negatively linked to pH and DIC. During high water flow, this may reflect the importance of Mg desorption/leaching from silicate suspended particles and dilution of underground waters interacting with soluble carbonate rocks. Finally, in summer,  26 Mg cannot be explained by any factor. This is consistent with simultaneous action of counter-balancing processes the secondary clay precipitation in soil fluids and the uptake of Mg by vegetation.

Discussion

Mg isotope variations in riverine waters over the hydrological year

As it has been argued earlier [START_REF] Bochkarev | Hydrochemistry of Rivers of Eastern Siberia (Gidrokhimiya rek Vostochnoi Sibiri)[END_REF][START_REF] Alekin | Foundations of Hydrochemistry (Osnovy Gidrokhimii)[END_REF][START_REF] Pokrovsky | Trace element fractionation and transport in boreal rivers and soil porewaters of permafrost-dominated basaltic terrain in Central Siberia[END_REF] the chemical composition of river waters in Siberian Rivers is the result of the combination of two major sources: i) leaching from the surface organic-rich soil layer including plant litter and ii) dissolution of minerals in deep soils and in the base rock; the latter delivers the mineral constituents to the river via taliks [START_REF] Anisimova | Cryohydrochemical Features of the Permafrost Zone[END_REF][START_REF] Bagard | Seasonal variability of element fluxes in two Central Siberian rivers draining high latitude permafrost dominated areas[END_REF][START_REF] Bagard | Biogeochemistry of stable Ca and radiogenic Sr isotopes in a larch-covered permafrost-dominated watershed of Central Siberia[END_REF]. It has to be noted that, due to the complexity of large river lithology, the impact of rock distribution as derived from geological maps can be compromised by river feeding from both surface (basalts) and deep (carbonates) reservoirs [START_REF] Pokrovsky | Basalt weathering in Central Siberia under permafrost conditions[END_REF]Mavromatis et al., 2014b). However, permafrost is present at the majority of the Yenisey basin. Its presence impacts the chemical composition of river waters that are influenced by the surface bedrock layers (0-10 m) rather than the deep rock lithology. Indeed, the extent and duration of permafrost is likely the main parameter controlling the relative contribution of each process over the hydrological year. For example, the major contributions from the upper soil layer and plant litter occur during the spring flood [START_REF] Pokrovsky | Silicon isotope variations in Central Siberian rivers during basalt weathering in permafrost-dominated larch forests[END_REF]Mavromatis et al., 2014b). Progressive thickening of the thaw layer achieves its maximum value at the end of summer (i.e. August).

The corresponding active layer depth limits the contribution of the top layers to the lateral export flux of cations to the river. The dissolved products of chemical weathering of thawed soils and leaching of plant litter are delivered to the river via so called suprapermafrost flow, a water flux travelling at the base of active layer, over the frozen horizons. The 87 Sr/ 86 Sr composition of water samples collected from the same tributaries during June of 2012 suggests a similar source of Sr in the riverine waters regardless of the season (Fig. 3), and this composition is not affected by secondary mineral precipitation reactions [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. For example, the 87 Sr/ 86 Sr ratios of N. Tunguska river exhibit variations of 0.0002 among seasons and reflect the 87 Sr/ 86 Sr ratios of the draining rock, the Patom paleobasin dolostones (i.e. 0.7080-0.7085; [START_REF] Pokrovsky | Co-variation of Mg and C isotopes in late Precambrian carbonates of the Siberian Platform: A new tool for tracing the change in weathering regime?[END_REF]. We note here however that differences in the contributions of each source, evidenced from Sr isotope variations, do not necessarily result in variations in elemental ratio composition of riverine waters. Indeed, the linear trend presented in Fig. 2 suggests that the origin of Mg and Ca varies among three seasons. This elemental ratio reflects mixing of two or more sources such as Precambrian dolomite, basalts, granites and larch litter. Note that some impact of atmospheric dust on seasonal variation in radiogenic Sr cannot be ruled out given that particulate deposition of Sr is comparable with its dissolved form in atmospheric snow of central Siberia [START_REF] Shevchenko | Impact of snow deposition on major and trace element concentrations and fluxes in surface waters of Western Siberian Lowland[END_REF]. At the same time, a single slope of the dependences shown in Fig. 2 suggests similar Mg/Ca ratio in the different solute pools as it has been highlighted earlier for bedrocks, soil and plant litter of Central Siberian catchments [START_REF] Pokrovsky | Basalt weathering in Central Siberia under permafrost conditions[END_REF].

Further, the dissolution of riverine suspended matter (RSM) within the main channel can represent sizeable source of dissolved elements to the riverine waters, thus providing important source of solutes to the aqueous phase. The increase in active layer thickness over the frost-free season leads to progressive decreasing of the depth of suprapermafrost flow, which traverses organic horizons in spring and mineral soil horizons towards the end of summer-autumn. The contribution of riverine suspended matter as element source in the fluid phase is expected to be at maximum during the spring flood, when the coastal abrasion and flood zone sediment resuspension are the highest. As a result, the delivery of cations to the river water is strongly controlled by the season, via organic versus mineral soil and bedrock contributions, and related to river discharge or local storm events via mobilization of suspended matter and plant debris. However, the isotopic signatures of Mg and Sr in the whole Yenisey River watershed over the hydrological year do not follow a simple relationship with season (discharge), unlike the cation concentrations. Three reasons may be responsible for such a decoupling. First, the permafrost is not homogeneously distributed in the Yenisey drainage area that spans over 2000 km in the S-N gradient from lake Baikal in the south to the Arctic Ocean (Fig. 1). In fact, the watershed of some river basins such as Kem and Kas are not affected at all by permafrost. As a result, there may be a possible effect on the δ 26 Mg value of riverine waters from the weathering of the organic rich layer and leaching of plant litter that is generally depleted in 26 Mg compared to the host rock [START_REF] Opfergelt | Magnesium retention on the soil exchange complex controlling Mg isotope variations in soils, soil solutions and vegetation in volcanic soils, Iceland[END_REF][START_REF] Teng | Magnesium Isotope Geochemistry[END_REF].

A second reason of Sr-Mg isotope decoupling is illustrated in Fig. 5 where a cross-plot of the isotopic composition of riverine waters between the winter campaign in March 2015 vs. the summer campaign during August 2015 suggests an enrichment of most summer samples with 26 Mg over winter samples. Such an enrichment is more evident for samples retrieved from Sym, Dubches and B. Pit rivers, draining terrigenous silicates and not affected by deep carbonate rocks and permafrost whereas it is negligible for P. Tunguska, N. Tunguska and Vorogovka rivers, draining through basalts or carbonates, and fed by deep underground taliks in winter. We note here that the major effect on the isotopic composition of Yenisey river and its tributaries can be anticipated to be strongly influenced by the bedrock composition occur during winter baseflow, when the rivers are fed by taliks through unfrozen paths. The isotopic signal of bedrock can be blurred during summer flow due to removal of isotopically light Mg via i) plant uptake (Mavromatis et al., 2014b) and ii) secondary clay precipitation in the interstitial soil solutions (soil porewaters) or shallow subsurface water reservoirs.

Finally, a third reason of isotope decoupling can be assessed by the δ 26 Mg values of riverine samples collected during open water period (May to October) and exhibit a good correlation with the riverine suspended material (RPearson = 0.44 at p < 0.05; Fig. 6). Indeed, 26 Mgenriched waters collected in spring (May) and summer (August) exhibit increasing RSM concentrations. We attribute this isotopically light composition of spring and summer waters to the extensive dissolution of RSM in the river channel. The RSM is composed of silicates (clays) and organic debris such as plant litter [START_REF] Pokrovsky | Basalt weathering in Central Siberia under permafrost conditions[END_REF]2013). Both have  26 Mg close to zero (-0.2±0.2‰, Mavromatis et al., 2014b) which is released to aqueous solution without substantial fractionation.

Isotopic composition of Mg flux to the Arctic Ocean

The Yenisey is the world's sixth largest river in terms of discharge and the largest contributor of freshwater to the Arctic Ocean, providing 18% of the total annual river discharge [START_REF] Holmes | Climate change impacts on the hydrology and biogeochemistry of Arctic Rivers[END_REF]. It drains through rocks of highly variable lithology and exhibits contrasting permafrost, forest, and wetland coverage. The western tributaries of Yenisey River drain through thawed and frozen peatlands of Western Siberian Lowland and as such represent many other permafrost-affected Artic rivers of low runoff (e.g. Pechora, Ob, Nadym, Pur and Taz). On the other hand the large eastern tributaries of Yenisey drain through granites, basalts and carbonates of mountainous regions with high runoff thus representing the other Central and Eastern Siberian rivers of the Lena basin. As such, the obtained results can provide first-order estimates of isotopic composition of Mg flux from the largest Arctic river. During the sampling campaigns of 2015, the riverine water samples have been collected from the main channel of Yenisey as north as 66.5°N, representing ca. 98% of the total river watershed, and their δ 26 Mg values were -1.36±0.04 ‰, -1.13±0.02 ‰, -1.13±0.01 ‰ for winter (March), spring (May) and summer (August) campaigns, respectively (Fig. 4). These values suggest an 0.23‰ difference in the Yenisey water δ 26 Mg value between winter and spring. Partially, this difference may be due to release of 26 Mg from the RSM given that the RSM concentration in winter is an order of magnitude lower than that during spring flood, as reported at the Yenisey gauging station [START_REF] Mcclelland | Particulate organic carbon and nitrogen export from major Arctic rivers[END_REF]. At the same time, the rivers most likely to be affected by underground waters draining carbonate rocks and feeding the river during winter (Vorogovka, P. Tunguska, N. Tunguska), the δ 26 Mg was virtually the same (i.e. 0.04‰ variation) in winter and summer. The difference between summer and winter values was not systematically different among tributaries. For N. Tunguska, Kas, P.

Tunguska, Vorogovka, this difference was < 0.5‰, whereas Sym, B. Pit, Tis, Dubches and Garevka exhibited up to 0.27 ‰ lower isotopic composition in winter compared to summer.

As such, we cannot attribute the observed differences to one single factor such as carbonate rocks (N. Tunguska, Vorogovka, but also B. Pit), permafrost (N. Tunguska, Garevka) or vegetation (all rivers). Altogether, the Yenisey isotopic measurements of this and our previous study [START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF], together with those reported by [START_REF] Tipper | The magnesium isotope budget of the modem ocean: Constraints from riverine magnesium isotope ratios[END_REF] for the Lena river suggest that Siberian rivers are characterized by an isotopic composition lower compared to that of seawater or the Arctic Ocean (i.e -0.82‰; [START_REF] Teng | Magnesium Isotope Geochemistry[END_REF]. It can reasonably be suggested that the parameters controlling the low isotopic composition of Yenisey and likely Lena is the weathering of the highly reactive Precambrian dolomites, which are abundant in the southern Siberian Platform and are characterized by Mg isotope compositions as low as -2.5‰ [START_REF] Pokrovsky | Co-variation of Mg and C isotopes in late Precambrian carbonates of the Siberian Platform: A new tool for tracing the change in weathering regime?[END_REF]. Indeed, hydrochemical analyses of present day alkalinity (DIC) features in the Yenisey River demonstrate similar contributions of carbonate and silicate weathering (53 and 47%, respectively, [START_REF] Tank | Landscape-level controls on dissolved carbon flux from diverse catchments of the circumboreal[END_REF]. As such, the Mg isotopic signal of the Yenisey River may be explained by the linear mixture of carbonate that span in the range -2.0 ‰ ≤ δ 26 Mgcarbonate ≤ -2.5 ‰ and silicate (0.0‰ ≤ δ 26 Mgsilicate ≤ -0.2‰) end members providing an average of ca. -1.2±0.1‰.

Although elemental fluxes in boreal and permafrost-affected rivers are known to exhibit strong seasonal variations similar to water discharge [START_REF] Guo | Speciation and fluxes of nutrients (N, P, Si) from the upper Yukon River[END_REF][START_REF] Guo | Sources and export fluxes of inorganic and organic carbon and nutrient species from the seasonally ice-covered Yukon River[END_REF][START_REF] Bagard | Seasonal variability of element fluxes in two Central Siberian rivers draining high latitude permafrost dominated areas[END_REF]2013), the magnesium isotope composition of Yenisey over the hydrological year exhibits rather small variability, ca. within 0.2‰, whereas the Mg concentration at the Yenisey river gauging station ranges from 62 to 250 μM L -1 [START_REF] Holmes | Arctic Great Rivers Observatory. Water Quality Dataset[END_REF]. In order to provide a first-order estimation of the average annual isotope composition of Mg discharge flux by the Yenisey River to the Arctic Ocean, we can use discharge weighted fluxes as: δ 26 Mgannual = δ 26 Mgspring × 0.29 + δ 26 Mgsummer × 0.265 + δ 26 Mgwinter × 0.445

(1)

where the subscripts ''spring" (i.e. May to June), ''summer" (i.e. July to September) and ''winter" (i.e. October to April) denote the isotopic composition of Yenisey River water during the corresponding hydrological period, whereas the fractions of the annual fluxes for 2006), but about 0.4‰ lighter compared to the δ 26 Mg of seawater (i.e -0.82‰; [START_REF] Teng | Magnesium Isotope Geochemistry[END_REF].

Whether the 0.15‰ difference in δ 26 Mg of global runoff and the Yenisey river is significant cannot be unequivocally resolved. However, it is possible that Siberian permafrostbearing rivers such as Yenisey (this study) and Lena (δ 26 Mg = -1.28±0.08 ‰; [START_REF] Tipper | The magnesium isotope budget of the modem ocean: Constraints from riverine magnesium isotope ratios[END_REF]

Conclusions

The Yenisey river main channel, as well as it's large and medium tributaries were sampled over three hydrological periods, spring, summer and winter. This allowed to assess seasonal variations in Mg isotopic ratios and to relate δ 26 Mg values to river discharge, landscape and lithological parameters of watersheds. Contrary to our expectations, the spring flood (May) and winter (March) period, two most different months in terms of discharge and degree of lithological impact on element source in the river water, did not show any measurable difference in δ 26 Mg signature of the river water (±0.1‰) for both the Yenisey tributaries and its main channel. Moreover, the summer season which is characterized by maximum impact of vegetation and maximally thawed soils, was not significantly (> 0.2‰) different from the other seasons. Despite these relatively homogeneous isotopic compositions of the Yenisey basin in space and time, some fine features of Mg isotope fractionation were revealed.

Some impact of river suspended matter on δ 26 Mg in the river water was observed via ca. 0.6‰ increase in δ 26 Mg accompanied with 5 times RSM concentration increase. A 0.2‰ difference in δ 26 Mg of Yenisey main channel between summer and spring (-1.36 and -1.13‰, respectively) could be attributed to removal of heavy isotope by biotic processes at the watershed and in the main stream. Direct lithological impact on Mg isotope composition is not seen, or the effect of dominant silicate (isotopically heavy) rock dissolution is completely masked by the high reactivity of isotopically light dolomites and limestones. The average annual isotopic flux of Mg by the Yenisey River to the Arctic Ocean is equal to -1.25‰ which is ~0.15‰ lower than the world average. This value is likely within the natural variation of isotopic signature in rivers. Together with the low value of the Lena river reported by [START_REF] Tipper | Calcium and magnesium isotope systematics in rivers draining the Himalaya-Tibetan-Plateau region: Lithological or fractionation control? Geochim[END_REF], this may signify a pronounced influence of isotopically light Precambrian dolomites and limestones for these two Siberian rivers.

The results of this study cast certain doubts on the usefulness of Mg isotope in rivers as tracers of weathering processes and assessing lithological impact on isotopic signature of riverine Mg flux, at least in the context of forested and permafrost-dominated watershed with highly variable lithology. Even minor proportion of isotopically light, highly reactive dolomites and limestones are capable to completely offset the impact of less reactive, isotopically heavy silicates. Further, because several drastically different processes such as formation of secondary clays, carbonate precipitation, or the biological uptake, are characterized by similar isotopic shift, the overall impact of these processes on Mg riverine isotopic signature may not be distinguishable.

  Mg have been calculated after[START_REF] Holmes | Seasonal and Annual Fluxes of Nutrients and Organic Matter from Large Rivers to the Arctic Ocean and Surrounding Seas[END_REF] similar to[START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF]. For the spring isotopic composition we use here the average of the measured Mg isotope composition of the post spring (June) and spring (May) sampling campaigns in years 2012 and 2015, respectively (i.e. -1.21±0.08‰). The resulting flux-averaged annual isotope signatures of Mg from the Yenisey River to the Arctic Ocean is thereby estimated to be δ 26 Mgannual = -1.25  0.15‰. This value is within uncertainty similar to that estimated earlier by[START_REF] Mavromatis | The effect of permafrost, vegetation, and lithology on Mg and Si isotope composition of the Yenisey River and its tributaries at the end of the spring flood[END_REF] and[START_REF] Hindshaw | Seasonal change of geochemical sources and processes in the Yenisei River: A Sr, Mg and Li isotope study[END_REF] as well as with the mean global runoff of -1.09‰ estimated earlier byTipper et al. (

  are indeed enriched in lighter Mg isotopes compared to the world rivers, even though the discharge-weighted δ 26 Mg for the world rivers is lacking. Because preferential removal of heavier isotopes by vegetation would require strong non-stationary conditions which is not the case in Siberia, we argue that the dissolution of carbonate (dolomite) rocks in the watersheds of Central and Eastern Siberian rivers is the main cause of relatively negative δ 26 Mg values of these rivers. In this regard, assessment of Mg isotope signature in large Siberian rivers not subjected to strong carbonate influence such as Ob, Pur and Taz of western Siberian lowland is essential for detailed constraining of the Mg isotopic budget of the Arctic Ocean.

Table 2 :

 2 Sampling date, geographic coordination of the stations, stable Mg and radiogenic Sr composition of riverine waters together with concentrations of Ca, 703 K, Mg, Na, Sr, SO4, Cl and DIC in μM L -1 and riverine suspended matter (RSM) for samples taken in the 2015 campaigns. The analytical uncertainty of the 704 87 Sr/ 86 Sr analyses has been estimated as ±0.000011 based on replicate analyses of SRM987 reference material. 705

	Sampling		Sampling	δ 25 Mg	2σ	δ 26 Mg	2σ	87 Sr/ 86 Sr	Ca	K	Mg	Na	Sr	SO4	Cl	DIC	RSM (mg/L)	Latitude ( o N) Longtitude
	station		date	(‰)		(‰)												( o E)
	Summer
	E001	Tugulan	05/08/15 -0.43	0.01 -0.82	0.10 0.708727 469	18 189	176	0.86	24.79	1.76	305.70	4.13	60.79	89.78
	E002	Kem'	03/08/15 -0.46	0.04 -0.90	0.02 0.709449 1382 33 411	360	3.05	9.87	15.23	477.77		58.52	92.08
	E003	Turukhan	08/08/15 -0.45	0.04 -0.88	0.08 0.708872 213	6	125	88	0.31	6.68	6.73	231.85	4.58	65.9	87.58
	E004	P. Tunguska	06/08/15 -0.64	0.03 -1.24	0.06 0.708998 891	26 492	816	8.01	582.91	255.99	397.94	1.73	61.62	90.18
	E005	Kiya	04/08/15					0.713971 226	8	97	67	0.38	2.17	19.61	193.75	1.77	59.16	91.50
	E006	Dubches	05/08/15 -0.52	0.05 -1.01	0.05 0.708880 408	11 258	148	0.57	3.48	10.60	279.52	12.75	60.99	89.61
	E007	Sukhaya	08/08/15					0.708670 301	5	164	155	0.55	82.88	35.39	203.44	1.43	65.16	88.00
		Tunguska
	E008	Miroedikha	08/08/15					0.708725 590	4	374	98	0.52	5.89	8.35	387.87	1.58	65.6	88.07
	E009	Kas	04/08/15 -0.47	0.02 -0.91	0.01 0.709619 742	20 246	172	1.33	4.15	5.54	356.88	3.80	59.99	90.55
	E010	Bakhta	06/08/15 -0.50	0.01 -0.98	0.02 0.708651 560	12 233	180	1.96	72.66	136.57	323.77	1.07	62.48	89.03
	E011	Garevka	04/08/15 -0.48	0.02 -0.94	0.02 0.727949 96	10 39	61	0.18	2.64	10.70	119.33		59.85	90.79
	E012	N. Tunguska	08/08/15 -0.45	0.04 -0.84	0.04 0.708540 553	14 173	898	1.94	1133.96	79.30	246.39	3.73	65.80	88.09
	E013	Elogyi	07/08/15 -0.51	0.04 -0.98	0.02 0.708580 332	10 206	197	0.48	3.66	7.25	383.44	3.87	63.19	87.75
	E014	Angara	03/08/15 -0.60	0.02 -1.17	0.01 0.709293 658	28 231	356	1.94	230.02	93.55	311.95	2.12	58.07	93.08
	E015	N. Syrnikha	05/08/15					0.711289 461	7	266	42	0.60	3.39	98.57	299.57		60.41	90.13
	E016	Sym	05/08/15 -0.39	0.02 -0.78	0.02 0.709603 244	14 106	107	0.50	3.56	7.63	191.29	9.02	60.25	90.05
	E017	Bolshoi Pit	03/08/15 -0.63	0.01 -1.21	0.01 0.710689 311	10 135	65	1.36	10.57	57.67	230.11	4.18	59.03	91.73
	E018	Vorogovka	05/08/15 -0.78	0.06 -1.49	0.04 0.709919 430	6	221	36	0.67	3.96	31.16	269.03		60.79	89.88
	E019	Komsa	07/08/15 -0.61	0.03 -1.17	0.07 0.708735 221	8	147	90	0.51	23.75	62.48	391.51	2.36	63.26	87.72
	E020	Tis	04/08/15 -0.72	0.01 -1.38	0.03 0.717517 214	9	133	60	0.32	4.03	24.63	186.49	1.42	59.55	90.92
	E021	Yenisey 1	03/08/15					0.708354 482	17 111	111	1.07				2.35	58.02	93.20
	E022	Yenisey 2	03/08/15 -0.66	0.03 -1.27	0.03 0.708841 623	24 195	286	1.81					59.00	91.71
	E022*			-0.67	0.04 -1.31	0.05
	E023	Yenisey 3	05/08/15					0.708847 594	21 179	202	1.45				2.76	60.29	90.12
	E024	Yenisey 5	06/08/15					0.708923 542	20 164	236	1.54				2.08	61.58	90.14
	E025	Yenisey 6	06/08/15 -0.70	0.02 -1.31	0.02 0.708969 679	23 242	353	2.81				3.82	62.46	88.97
	E026	Yenisey 7	07/08/15					0.708955 634	22 269	395	3.30				2.46	63.25	87.69
	E027	Yenisey 8	07/08/15					0.708920 690	23 222	311	2.32				3.40	64.13	87.54
	E028	Yenisey 9	05/08/15					0.708939 640	23 240	334	2.45				3.97	65.77	87.99
	E029	Yenisey 10	09/08/15 -0.58	0.01 -1.13	0.01 0.709870 637	20 243	462	2.64				5.35	66.46	87.23
	Spring
	E030	Sym	27/05/15					0.711693 46	16 20	25	0.11	1.02	8.96	11.75	29.36	60.25	90.05
	E031	Kas	26/05/15					0.709652 117	20 40	34	0.28	3.02	13.49	33.93	23.48	59.99	90.55
	E032	Komsa	29/05/15					0.708730 85	5	51	23	0.16	2.94	8.59	40.49		63.26	87.72
	E033	Kem'	25/05/15 -0.44	0.07 -0.88	0.04 0.709598 587	33 185	167	1.33	8.13	22.31	253.93		58.52	92.08

Acknowledgments

The study was supported by RFFI (RFBR) grant No 19-55-15002 and by the French national programmes INSU-LEFE and INSU-SYSTER.


Sophie Lantheaume
email: lantheaume.sophie@hotmail.fr

Lydia Fernandez
email: lydia.fernandez@univ-lyon2.fr

Stéphane Lantheaume

Ladislav Moták
email: ladislav.motak@univ-amu.fr

Stéphanie Blois-Da Conceição
email: stephanie.bloisdaconceicao@univ-lyon2.fr

Quality of Life in Patients with Non-metastatic Breast Cancer: Evolution during Follow-up and Vulnerability Factors Running head: Vulnerability Factors in Non-metastatic breast cancer and quality of life

Keywords: Anxiety, Body image, Breast cancer, Depression, Quality of life, Social support

1) Objective

In women with breast cancer, many disease-related psychosocial factors directly affect quality of life (QoL) during and after treatment. The aims of this prospective study were to evaluate the psychosocial factors affecting QoL and its dimensions, to study their evolution over time, and to determine the factors associated with this evolution. (2) Methods Thirty women with nonmetastatic breast cancer were asked to complete a number of questionnaires evaluating QoL and its dimensions, symptoms of anxiety and depression, body image, social support, and coping strategies immediately after their diagnosis of breast cancer (T1), at the end of treatment (T2) and 6 months after the end of treatment (T3). (3) Results Level of education, mastectomy, and hormonotherapy all had an impact on QoL. QoL and its dimensions changed over time. Coping strategies, social support, body image, and symptoms of anxiety and depression were predictive factors for QoL. (4) Conclusion The identification of these predictive factors should help medical teams to identify the patients who are most vulnerable and susceptible to poor QoL. In women with breast cancer, it is essential to identify and treat any changes in patients' need for support in an appropriate manner, both during the course of therapy and particularly during remission.

BACKGROUND

The diagnosis of breast cancer is destabilising for women, who are faced with the fear of death, questions about their femininity, and changes in their quality of life (QoL) [START_REF] Lantheaume | Qualité de vie de patiente en rémission traitées pour un cancer du sein non métastasé, selon la surveillance médicale classique ou alternée[END_REF][START_REF] Wöckel | Predictors of course of quality of life during therapy in women with primary breast cancer[END_REF]. QoL has been defined as "an aggregate of representations based on the state of health, physiological state, well-being and satisfaction with life" [START_REF] Mackeigan | Overview of health-related quality-of-life measures[END_REF]. Psychological readjustment (acceptance of new body image, fear of relapse, etc.), along with changes in relationships with others (becoming closer, keeping one's distance, professional adaptation, etc.) should be implemented as needed in each individual case to assure a good QoL [START_REF] Lantheaume | Rémission, guérison… et rétablissement dans l'expérience du cancer du sein[END_REF]. Great variability in these readjustments is observed, which is linked to disease-related psychosocial factors which appear to directly influence the QoL of patients both during and after treatment. Some authors have demonstrated the importance of sociodemographic factors (e.g., age, residential environment, familial or professional situation) [START_REF] Bloom | Then and now: quality of life of young breast cancer survivors[END_REF][START_REF] Hopwood | A body image scale for use with cancer patients[END_REF][START_REF] Hopwood | The impact of age and clinical factors on quality of life in breast cancer: an analysis of 2208 women recruited to the UK START Trial[END_REF]; treatment-related factors (e.g., type of surgery, use of chemotherapy, hormonotherapy) [START_REF] Dupont | Qualité de vie et sexualité des femmes atteintes d'un cancer du sein : impact de la chirurgie et de l'hormonothérapie[END_REF]; time-related factors [START_REF] Mols | Quality of life among long-term breast cancer survivors: a systematic revue[END_REF]; factors linked to psychological adaptation (e.g., coping strategies and social support) [START_REF] Mccaul | Coping and adjustment to breast cancer[END_REF]; and psychological or psychiatric issues (e.g., body image, anxiety, and depression) [START_REF] Trzepizur | Facteurs prédictifs de souffrance psychique et de qualité de vie à long terme chez des patientes ayant été traitées pour un cancer du sein non métastatique[END_REF]. An understanding of all these factors is necessary in order to improve the tailored psychosocial support offered to individual patients.

The aim of the current study was to develop a predictive model of QoL in patients with breast cancer, drawn from the transactional, integrative, and multifactorial model proposed by Bruchon-Schweitzer and Dantzer [START_REF] Bruchon-Schweitzer | Introduction à la psychologie de la santé[END_REF] (Figure 1). The model posits that two types of elements should be distinguished: (i) predictive factors (i.e., personal and environmental factors that increase vulnerability or promote resilience); and (ii) transactional processes (that is, factors such as perceived stress, control, or even adjustment strategies, accounting for the cognitive, emotional, and behavioural activity of the individual with the disease). These transactional processes have been assumed both to impact differently on QoL, and play a role in mediating or moderating variables. In other words, they have been shown to modify the impact of predictive factors on the variable that we seek to explain. Additionally, we also evaluated the psychosocial determinants of QoL in our patients, studied the changes in QoL over time,

5

and investigated the factors associated with these changes. We propose specific support adapted to patients with breast cancer which accounts for the above-mentioned vulnerability factors.

This study is original in its investigation of the psychosocial variables contributing to a QoL in a prospective manner. Data on the factors influencing QoL have rarely been collected prospectively in these patients [START_REF] Wöckel | Predictors of course of quality of life during therapy in women with primary breast cancer[END_REF]. A better understanding of QoL and its predictive factors is crucial, because while attention and emotional support can be put in place for the most vulnerable women, it is first necessary to identify them as early as possible [START_REF] Trzepizur | Facteurs prédictifs de souffrance psychique et de qualité de vie à long terme chez des patientes ayant été traitées pour un cancer du sein non métastatique[END_REF][START_REF] Härtl | Personality traits and psychosocial stress: Quality of life over 2 years following breast cancer diagnosis and psychological impact factors[END_REF][START_REF] Schou | Multiple predictors of health-related quality of life in early stage breast cancer. Data from a year follow-up study compared with the general population[END_REF].

METHODS

Study population

Thirty patients, aged 30-78 years (mean ± SD: 52.1 ± 11.7 years), with early non-metastatic breast cancer, were recruited voluntarily for this study. There were only three exclusion criteria: presence of recognised psychosis, patients in relapse, and presence of metastases.

Tools and procedure

The study was carried out according to the principles and rules laid down in Caverni's code of conduct for investigators and the Declaration of Helsinki [START_REF] Caverni | Pour un code de conduite des chercheurs dans les sciences du comportement humain[END_REF][START_REF]Déclaration d'Helsinki de L'Association Médicale Mondiale Principes éthiques applicables à la recherche médicale impliquant des êtres humains[END_REF]. The study was started before the Loi Jardé changes (art. L. 1121-1 and R. 1121-1 of the CSP). The study did not seek to develop biological or medical knowledge. All the participants received an information letter and gave their written informed consent before taking part.

The study was longitudinal and prospective. Data were collected at the following three distinct time points. Time 1 (T1): between the announcement of the diagnosis and the first treatment for breast cancer; time 2 (T2): at the end of anti-cancer treatments (i.e., surgery, chemotherapy and/or radiotherapy); and time 3 (T3): 6 months after the end of treatments (T2 + 6 months).

Several scales were used to measure the following variables:

Quality of life (QoL)

To measure QoL, we used FACT-B (Functional Assessment of Cancer Therapy-Breast) [START_REF] Brady | Reliability and validity of the functional assessment of cancer therapy-breast quality-of-life instrument[END_REF], based on an initial 27 items derived from FACT-G (General) [START_REF] Cella | The functional assessment of cancer therapy scale: development and validation of the general measure[END_REF], and 10 breast cancer-specific items. An overall 6 score for QoL, as well as four partial scores (physical well-being (PWB), familial and social well-being (FSWB), emotional well-being (EWB), and functional well-being (FWB) were obtained. The higher the score, the better the patient's well-being.

Symptoms of anxiety and depression

Symptoms of anxiety and depression were measured using the HADS (Hospital and Depression Scale) [START_REF] Zigmond | The hospital anxiety and depression scale[END_REF]. The HADS is composed of 14 items. For the three scores (depression, anxiety, and overall), we used the following threshold values: (i) a score of ≤7 suggested the absence of an anxious or depressive state or of symptoms of anxiety and depression; (ii) a score of between 8 and 10 suggested a possible anxious or depressive state or possible symptoms of anxiety and depression; and (iii) a score of ≥11 suggested a definite anxious or depressive state or definite symptoms of anxiety and depression.

Body image

Body image was measured using the BIQ questionnaire (Body Image Questionnaire) [START_REF] Bruchon-Schweitzer | Dimensionality of the body image: the BIQ[END_REF]. A total score for satisfaction with body image ranging from 95 (favourable body image) to 19 (unfavourable body image) was obtained by adding up the responses.

Social support

Social support specific to cancer was measured using the QSSS-c (cancer-Specific Social Support Questionnaire) [START_REF] Segrestan | A cancer specific scale of social support for patients and relatives[END_REF]. Four scores were evaluated: (i) emotional social support (ESS), with scores ranging from 9 to 45; (ii) material and distractive social support (MDSS), with scores ranging from 5 to 25; (iii) informative social support (ISS), with scores ranging from 2 to 10; and (iv) negative social support (NSS), with scores ranging from 4 to 20. A high score in each of these dimensions indicated that the individual receives a high level of support from others.

Coping strategies

Coping strategies were measured using the 27-item WCC scale (Ways of Coping Checklist) [START_REF] Vitaliano | The ways of coping checklist: Revision and psychometric properties[END_REF]. Three scores were obtained, relating to: problem-focused coping strategies, emotion-focused coping strategies, and social support-focused coping. A high score indicated a strong use of coping strategies.

Statistical analyses

ANOVA (analysis of variance), Student's t test and Linear regression analyses were used. In all the statistical tests, the threshold of significance was p<0.05, but marginally significant effects (p≤0.07) were also noted. All statistical analyses were carried out using SPSS.22 © .

RESULTS

Characteristics of the study population

The sociodemographic and clinical characteristics of the study population are shown in Tables 1a and1b. Half of the participants lived in rural areas, and over half have obtained a secondary school certificate. The majority of women were married or in a civil partnership. Half of the patients received adjuvant chemotherapy as treatment.

The QoL of the patients appeared to be relatively good at inclusion (mean ± SD score: 89.3 ± 23.9 out of 148).

Role of sociodemographic factors, clinical variables, and time

The complete results relative to this section are summarised in Figures 2a to 2d and Table 2. Here, we only offer the main details of our analyses.

Level of education was observed to have a significant impact on QoL. Indeed, patients with a secondary school certificate had a global QoL that increased in the period from diagnosis to 6 months after the end of the treatments. FSWB decreased after treatment in patients who had undergone a mastectomy. Patients receiving hormonotherapy had a lower PWB than those who were not taking hormones. However, in patients who were not taking hormonotherapy, FWB decreased after treatment.

Finally, in the period from the diagnosis to 6 months after the end of the treatments, global QoL, EWB and FWB increased; in contrast, FSWB decreased.

Role of transactional variables

The complete results relative to this section are summarised in Table 3. Here, we only offer the main details of our analyses.

Emotion-focused coping was a predictive factor for a poorer QoL and low EWB at diagnosis. It also tended to predict a poorer QoL and lower EWB after the end of the treatments, and QoL and EWB Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 8 appeared again as reliable predictors at 6 months after the end of the treatments. The same was true for FSWB and FWB at diagnosis, in that the more the patients used emotion-focused coping strategies, the less their FSWB scores increased. Seeking social support-focused coping appeared to be a predictive factor for high FSWB at diagnosis. At 6 months after the end of the treatments, this was marginal for FSWB and FWB. Problem-focused coping appeared to be a predictive factor for high EWB at diagnosis and good QoL, high FSWB, and high FWB after the end of the treatments.

NSS was a predictive factor for poor QoL at T3 and low FSWB after the end of the treatments, and the results persisted at 6 months after the end of the treatments. Emotional social support was a predictive factor for good QoL 6 months after the end of the treatments.

From the diagnosis to 6 months after the end of the treatments, depression was a predictive factor for a poor QoL, low PWB, and low FSWB as well as low EWB and low FWB at diagnosis; and low FWB at 6 months after the end of the treatments. Anxiety was a predictive factor for low EWB at diagnosis and 6 months after the end of the treatments. Body image was, or tended to be, a predictive factor for good QoL and high FWB at the end of the treatments as well as for high FWB at 6 months after the end of the treatments.

DISCUSSION

The aims of this study, which was carried out in a sample of women with non-metastatic breast cancer, were threefold: to evaluate the psychosocial factors relating to QoL; to study the changes in these factors over time; and to investigate the factors associated with these changes.

Our results showed a significant effect of level of education on QoL over time. Patients in our study who had a secondary school certificate reported a QoL which improved between diagnosis and 6 months after the end of treatments. These results concur with those in the literature that level of education is protective of QoL represents a variable of psychological adjustment [START_REF] Stein | Physical and psychological long-term and late effects of cancer[END_REF] and is associated with the development of a new way of looking at life [START_REF] Porter | Predicting negative mood state and personal growth in African American end white long-term breast cancer survivors[END_REF]. It also suggests that individuals with a higher level of education are more able to question their doctor and to better understand the information given, and also to look for additional information themselves. As a result, they have a more realistic view of the Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 9 treatments and their long-term side-effects, and consequently have fewer disappointments or surprises [START_REF] Stein | Physical and psychological long-term and late effects of cancer[END_REF].

We also found that the FSWB of patients who underwent a mastectomy decreased between the end of treatment and the 6-month follow-up. This was independent of the presence (or otherwise) of breast reconstruction. Mastectomy is associated with a poorer body image [START_REF] Hopwood | The impact of age and clinical factors on quality of life in breast cancer: an analysis of 2208 women recruited to the UK START Trial[END_REF] and therefore individuals consider themselves less physically attractive. These results concur with those in the literature [START_REF] Howes | Quality of life following total mastectomy with and without reconstruction versus breast-conserving surgery for breast cancer: A case-controlled cohort study[END_REF][START_REF] Zehra | Health related quality of life following breast reconstruction compared to breast conserving surgery and mastectomy among breast cancer survivors: A systematic review and meta-analysis[END_REF].

Particular attention should be paid to couples in whom a woman undergoes a mastectomy.

In our study, patients receiving hormonotherapy had a lower PWB than patients without hormonotherapy. Hormonotherapy can lead to early menopause, or to more severe symptoms associated with the menopause (hot flushes, sweating, fatigue, loss of libido, etc.) [START_REF] Stahlschmidt | Adherence and quality of life in women with breast cancer being treated with oral hormone therapy[END_REF]. It can be hypothesised that the emergence of these symptoms may negatively affect the PWB of patients. The literature has shown that there was no significant correlation between hormonotherapy and the physical, social, emotional, and functional domains of QoL [START_REF] Hopwood | The impact of age and clinical factors on quality of life in breast cancer: an analysis of 2208 women recruited to the UK START Trial[END_REF][START_REF] Dupont | Qualité de vie et sexualité des femmes atteintes d'un cancer du sein : impact de la chirurgie et de l'hormonothérapie[END_REF]. However, the impact of hormonotherapy on QoL was related to the type of treatment received [START_REF] Stahlschmidt | Adherence and quality of life in women with breast cancer being treated with oral hormone therapy[END_REF]. Patients treated with aromatase inhibitors will present with fewer symptoms and better functioning than patients treated with tamoxifen. Another surprising result in our study was that the PWB of patients without hormonotherapy decreased immediately after treatment and 6 months after. No study in the prior literature has explained this effect. It is possible that taking hormonotherapy reduces the functional consequences of the disease and its treatments by justifying fatigue, for example. Patients on hormonotherapy receive more help in the house because they are still considered to be receiving treatment, in contrast to patients who are not receiving hormonotherapy [START_REF] Dupont | Qualité de vie et sexualité des femmes atteintes d'un cancer du sein : impact de la chirurgie et de l'hormonothérapie[END_REF].

Our results showed a moderate evolution of QoL, EWB, and FWB between the diagnosis and the post-treatment 6-month follow-up, as well as a decrease in FSWB. Several previous studies have reported contradictory results [START_REF] Wöckel | Predictors of course of quality of life during therapy in women with primary breast cancer[END_REF][START_REF] Härtl | Personality traits and psychosocial stress: Quality of life over 2 years following breast cancer diagnosis and psychological impact factors[END_REF][START_REF] Schou | Multiple predictors of health-related quality of life in early stage breast cancer. Data from a year follow-up study compared with the general population[END_REF]. This variation can be explained by the persistence, in some patients, of physical and psychological problems for several months after the end of treatment (persistent side-effects, poor body image). It could also be linked to the way in which QoL has been measured [START_REF] Fayers | Quality of life: The assessment, analysis and interpretation of patientreported outcomes[END_REF].

The decrease in FSWB over time found in the present study supports previous studies demonstrating the difficulties encountered by patients within their social and family circles (in relation to the pursuit of Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 10 satisfaction of individual needs, adaptation to a change in role and way of life, the management of uncertainty, etc.) [START_REF] Lopes | The experience of male spouses in the context of breast cancer: a systematic review of the literature[END_REF]. When fear of the disease becomes more distant and life returns to normal, the focus of the family on the disease changes to distancing. It is also possible that awareness following the diagnosis and experience of the disease leads patients to question a number of factors, notably their relationship with their partner and the place in their life of certain friends [START_REF] Lopes | The experience of male spouses in the context of breast cancer: a systematic review of the literature[END_REF]. Support is necessary to help women and their partners resume their activities and roles [START_REF] Catania | Men's experience of their partners' breast cancer diagnosis, breast surgery and oncological treatment[END_REF].

Concerning the coping strategies used by the patients in our study, we found that emotionfocused coping appeared to be a predictive factor for low FSWB and FWB at diagnosis. These strategies also resulted in a poorer overall QoL and low EWB from diagnosis through to 6 months after the end of treatment. Emotion-focused coping may take diverse forms of expression (repression/avoidance of emotions, helplessness/hopelessness, denial, fatalism, etc.). The literature has shown that avoidance after diagnosis was accompanied by a decrease in physical, mental, and social QoL, and that avoidance decreased the meaning of a patient's life [START_REF] Mccaul | Coping and adjustment to breast cancer[END_REF]. In individuals with cancer, the belief that one's life has meaning is strongly correlated with QoL [START_REF] Tomich | Five years later: a cross-sectional comparison of breast cancer survivors with healthy women[END_REF]. Conversely, problem-focused coping appeared to be a predictive factor for high EWB at diagnosis, along with good QoL and high FSWB and FWB after treatment. These results are in line with those in the literature, which have shown that problem-focused coping is salutogenic in relation to overall QoL [START_REF] Cayrou | Les facteurs associés à l'ajustement mental des malades du cancer. Un exemple d'utilisation de la MAC[END_REF]. Finally, seeking social support-focused coping appeared to be predictive of high FSWB both at diagnosis and 6 months after the end of treatment, as well as high FWB 6 months after the end of treatment. The salutogenic side of seeking social supportfocused coping will to a great extent depend on the environment of the individual patient [START_REF] Stanton | Emotionally expressive coping predicts psychological and physical adjustment to breast cancer[END_REF]; in other words, the search for social support will only be beneficial if the environment of the patient is receptive and tuned in. Emotion-focused coping interacts with the receptivity of the patient's entourage. Thus, in women who can count on their close ones to talk freely about their cancer, emotion-focused coping will increase QoL, whereas in women whose entourage is not receptive, emotional expression is not linked to QoL [START_REF] Stanton | Emotionally expressive coping predicts psychological and physical adjustment to breast cancer[END_REF].

The literature demonstrates that the type of support given (emotional, material, etc.) should be appropriate to the nature of the stress (controllable or not), the source of support (medical, familial, etc.), Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 11 the time of the intervention (in the acute phase of stress or after) [START_REF] Helgeson | Social support and quality of life[END_REF], and also with the expectations of the patient [START_REF] Reynolds | Mismatches in social support and psychosocial adjustment to breast cancer[END_REF]. Alignment between the nature of support wished for and that which is received is important [START_REF] Helgeson | Social support and quality of life[END_REF][START_REF] Reynolds | Mismatches in social support and psychosocial adjustment to breast cancer[END_REF]. In our study, NSS was predictive of low FSWB after treatment and poor QoL both after treatment and 6 months later. NSS represents all unwanted supporting behaviours (e.g., support that invites the person to be optimistic is often badly received by patients, particularly at the time of diagnosis and cancer treatments). NSS may generate feelings of vulnerability, helplessness, incompetence, and loss of freedom, autonomy and control, and may also affect the individual's ability to face up to the situation [START_REF] Blois | Cancer, relations interpersonnelles et soutien social : une analyse psychosociale de l'expérience des patients[END_REF]. After treatment or the start of remission can be a particularly difficult period for patients [START_REF] Lantheaume | Rémission, guérison… et rétablissement dans l'expérience du cancer du sein[END_REF]. Their entourage often associates the end of treatment with the end of the disease, at which point they distance themselves from the patient. This distancing may be perceived as negative support which could act directly and negatively on the FSWB of patients (communication problems, changes in interpersonal relationships, distancing/over protection, etc.) and on FWB (decrease in daily physical and material help, etc.). The results of our study also demonstrate that ESS is a predictive factor for good QoL 6 months after the end of treatment. This type of social support will be more beneficial and more comforting during an uncontrollable event and during an acute phase of illness [START_REF] Brady | Reliability and validity of the functional assessment of cancer therapy-breast quality-of-life instrument[END_REF]. Emotional support (e.g., the presence and daily attentions of the patient's close family and friends via telephone calls, visits, invitations, etc., encouragement, sharing information on the history of the disease and the changes induced, and so on) attenuates the effects of stress to which patients are subjected, and improves mental well-being [START_REF] Bloom | Sources of support and the physical and mental well-being of young women with breast cancer[END_REF]. In order to facilitate the adaptation of patients during the post-treatment period, efforts should be made to understand and respond to their support needs during the entire cancer experience, particularly when the treatments are finished.

The results of our study demonstrate that depression is a predictive factor for a poorer QoL, low PWB, and low FSWB, both at diagnosis and 6 months after the end of treatment. It is also a predictive factor for low EWB at the time of diagnosis, as well as low FWB both at the time of diagnosis and 6 months after the end of treatment. These results support those in the literature [START_REF] Trzepizur | Facteurs prédictifs de souffrance psychique et de qualité de vie à long terme chez des patientes ayant été traitées pour un cancer du sein non métastatique[END_REF]. Anxiety also appeared to be a predictive factor for low EWB both at the time of diagnosis and 6 months after the end of treatment. These two periods are times of waiting and uncertainty (expectations of treatment, of the Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 12 first check-up mammogram, fear of relapse, etc.) which can prove to be as testing for the patient as diagnosis of the disease itself [START_REF] Lantheaume | Qualité de vie de patiente en rémission traitées pour un cancer du sein non métastasé, selon la surveillance médicale classique ou alternée[END_REF][START_REF] Lantheaume | Rémission, guérison… et rétablissement dans l'expérience du cancer du sein[END_REF][START_REF] Barreau | Une étude exploratoire sur le vécu psychologique des femmes lors de la surveillance des anomalies mammographiques probablement bénignes : à propos de 50 cas[END_REF]. The evaluation of symptoms of anxiety and depression is necessary throughout the entirety of the care pathway, particularly during the post-treatment period.

Finally, body image appears to be a predictor of good QoL after treatment, and high FWB both after treatment and 6 months later. Body image is an important component of QoL in women with breast cancer [START_REF] Hopwood | A body image scale for use with cancer patients[END_REF][START_REF] Bagheri | Body image and quality of life in female patients with breast cancer and healthy women[END_REF]. The ability to be able to carry out some tasks (work, enjoying life in general, appreciating hobbies, etc.) appears to depend on how patients perceive their body image, and may explain the impact on FWB of patients in our study. Specific support to work on body image (e.g., socio-aesthetics [START_REF] Saghatchian | La socio-esthétique en oncologie : impact des soins de beauté et de bien-être évalué dans une enquête nationale auprès de 1166 personnes[END_REF]) should therefore be implemented as early as possible.

Study Limitations

Our study has several limitations. First, the generalisability of our results is limited since our sample was relatively small and recruitment was limited to a single centre. A multicentre study could be proposed to control for the bias attributed to inter-establishment differences in management, in case these differences directly influence the QoL of patients. It also would be interesting to explore the evolution of variables over a longer follow-up period of 2-5 years, particularly as we showed an effect of time elapsed since diagnosis on QoL. The study sample is not representative of the usual breast cancer population. Indeed, the study is composed the half of patient with mastectomy. Therefore, some caution is needed when interpreting our results. Finally, while the use of mixed methodology has many positive aspects, carrying out a longitudinal study takes time and limits the number of recruited patients. This was a pilot study; the results we have obtained encourage us to continue the initial study, taking into account its present limitations.

Clinical Implications and recommendations

With regard to the clinical implications of this study, the results lead us to propose a number of recommendations, which are as follows. (i) The most vulnerable patients should be identified by assessing their level of education and level of anxiety and depression, both during treatment and up to 6 months after the end of treatment. These patients could, for example, receive closer paramedical followup by a coordinating nurse; (ii) support should be adapted to each couple (in sexology, for example),

CONCLUSION

This study investigated the variability in QoL of women with non-metastatic breast cancer using an integrative and multifactorial model [START_REF] Bruchon-Schweitzer | Introduction à la psychologie de la santé[END_REF]. The demonstration of the direct effects of some factors on QoL and its dimensions should help teams to identify vulnerable patients who are at the greatest risk of poor QoL. It is essential to identify, propose, and address their changes in needs in order to support patients with breast cancer in an appropriate manner during the course of their disease, particularly after treatment has ended and during remission.

  Lantheaume, S., et al.(2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 13 particularly when women with breast cancer have had a mastectomy; (iii) psychological consultations should be offered in the months following the end of treatment in order to assure ESS in this post-cancer phase; and (iv) the services of a socio-aesthetician should be integrated as early as possible after the diagnosis to overcome the problems of body image.

  Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y

  Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y

Lantheaume, S., et

  al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y

	7

Table 1a . Sociodemographic and clinical characteristics of the study population

 1a Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y

	Mastectomy	16 (53.3)
	Second treatment received
	None	5 (16.7)
	Chemotherapy	15 (50.0)
	Radiotherapy	9 (30.0)
	Hormonotherapy	1 (3.3)
	Third treatment received
	None	8 (26.7)
	Radiotherapy	15 (50.0)
	Hormonotherapy	7 (23.3)
	Breast reconstruction
	Yes	11 (36.7)
	No	19 (63.3)
	Sequelae after treatment
	None	4 (13.3)
	Pain	14 (46.6)
	Fatigue	8 (26.7)
	Physical limitations	1 (3.3)
	Other	3 (10.0)
	Characteristic	N (%)
	Residential environment
	Rural	15 (50.0)
	Urban	15 (50.0)
	Level of education
	< Secondary school certificate	13 (43.4)
	≥ Secondary school certificate	17 (56.7)
	Familial situation
	Married or civil partnership	19 (63.3)
	Widowed	2 (6.7)
	Cohabiting	5 (16.7)
	Divorced	4 (13.3)
	First treatment received
	Surgery	28 (93.3)
	Chemotherapy neo adjuvant	2 (6.7)
	Nature of surgery
	Lumpectomy	14 (46.7)
		18

Table 1b . Type of adjuvant treatment after lumpectomy or mastectomy

 1b

		Chimiotherapy	Radiotherapy	Hormonotherapy	Chimiotherapy	Radiotherapy	No
		and	and	only	and	only	additional
		radiotherapy	hormonotherapy	N (%)	radiotherapy	N (%)	treatment
		and	N (%)		N (%)		N (%)
		hormonotherapy
		N (%)
	Lumpectomy	4 (28.6)	6 (42.8)	1 (7.1)	7 (50.0)	0 (0.0)	0 (0.0)
	Mastectomy	7 (43.7)	1 (6.2)	11 (6.2)	0 (0.0)	3 (18.7)	5 (31.2)

Table 2 . Effect of time on quality of life (QoL) and its dimensions

 2

		T1	T2	T3	F	p	η 2
		(mean ± SD)	(mean ± SD)	(mean ± SD)
	QoL	89.3 ± 23.9	94.3 ± 17.7	99.7 ± 17.5	5.64	** 0.16
	FSWB	19.2± 5.8	17.9 ± 6.4	17.3 ± 5.8	3.18	*	0.10
	FWB	15.2 ± 6.7	16.6 ± 5.5	17.1 ± 5.1	5.06	** 0.15
	EWB	13.9 ± 6.3	16.2 ± 4.9	17.7 ± 4.0	10.23 *** 0.26
	Legend. QoL: quality of life; FSWB: familial and social well-being; FWB: functional well-being;
	EWB: emotional well-being; T1: post-diagnosis; T2: after treatment; T3: T2 + 6 months; F: Fisher's
	ANOVA; η² = eta² or correlation relationship: a value between 0.010 and 0.059 corresponds to a
	weak effect, a value between 0.06 and 0.138 to a moderate effect, and a value >0.138 means that the
		effect is strong; *p≤0.05; **p≤0.01; ***p≤0.001.

Table 3 . Effects of coping strategies, social support, and psychological factors (depression, anxiety, body image) on quality of life (QoL) and its dimensions

 3

	Predicted	Set of	Individual		T1	T2		T3
	construct	variables	predictors
				R²	β	R²	β	R²	β
			Problem-focused		--		.52 **		--
		Coping s.	Emotion-focused	.46 ***	-.72 ***	.45 ***	-.31 *	.41 ***	-.51 *
			SS-focused		--		--		--
			ESS						--
	QoL	Social s.	MDSS ISS	ns.		ns.		.20 *	----
			NSS						-.52 **
		Psychol. factors	Anxiety Depression	.68 ***	---.59 **	.45 ***	---.50 **	.54 ***	---.71 **
			Body image		--		.32 #		--
					T1	T2		T3
				R²	β	R²	β	R²	β
		Coping s.		ns.		ns.		ns.
		Social s.		ns.		ns.		ns.
	PWB
		Psychol. factors	Anxiety Depression	.23 *	---.59 **	.19 *	---.62 **	.22 *	---.67 **
			Body image		--		--		--
					T1	T2		T3
				R²	β	R²	β	R²	β
			Problem-focused		--	.64 ***		--
		Coping s.	Emotion-focused	.18 *	-.34 #	.45 ***	--	.17 #	--
			SS-focused		--		--		.45 #
			ESS				.52 **		.53 **
	FSWB	Social s.	MDSS ISS	ns.		.41 ***	----	.42 ***	----
			NSS				-.39 *		-.36 *
		Psychol. factors	Anxiety Depression	.16 *	---.52 *	.24 *	---.48 *	.30 **	---.63 **
			Body image		--		--		--
					T1	T2		T3
				R²	β	R²	β	R²	β
			Problem-focused		.38 ***		--		--
		Coping s.	Emotion-focused	.77 ***	-.69 ***	.32 **	-.37 *	.39 ***	-.49 **
			SS-focused		--		--		--
	EWB	Social s.		ns.		ns.		ns.
		Psychol. factors	Anxiety Depression	.68 ***	-.51 *** -.40 **	ns.		.36 **	-.54 ** --
			Body image		--				--
					T1	T2		T3
				R²	β	R²	β	R²	β
			Problem-focused		--		.40 *		--
		Coping s.	Emotion-focused	.16 #	-.49 **	.22 *	--	.27 **	--
			SS-focused		--		--		.40 #
	FWB	Social s.		ns.		ns.		ns.
		Psychol. factors	Anxiety Depression	.40 ***	---.71 ***	.32 **	----	.60 ***	---.47 **
			Body image		--		.48 *		.51 **

Legend. Coping s: coping strategies; Social s: social support; Psychol factors: psychological factors; SS-focused: social support focused-coping; ESS: emotional social support; MDSS: material and distractive social support; ISS: informative social support; NSS: negative social support; "R²"adjusted R²; "beta" -standardized coefficients; # -p < .10; * -p < .05; ** -p < .01; *** -p < .001.

funds, grants, or other support were received.

Availability of data and material: all the authors made sure that all dates and materials as well as software applications or custom codes support their published claims and comply with field standards.

Code availability: N/A. Authors' contributions: all the authors contributed to the study's conception and design. Material preparation and data collection were performed by Sophie Lantheaume. Data analysis was performed

Lantheaume, S., et al. (2021). Quality of life in patients with non-mestastatic breast cancer: Evolution during follow-up and vulnerability factors. Supportive Care in Cancer. doi.org/10.1007/s00520-021-06203y 2 by Ladislav Moták. The first draft of the manuscript was written by Sophie Lantheaume, and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.