Kévin Barré
email: kevin.barre@edu.mnhn.fr

Arthur Vernet

Clémentine Azam

Isabelle Le Viol

Agathe Dumont

Thomas Deana

Stéphane Vincent

Samuel Challéat

Christian Kerbiriou

Landscape composition drives the impacts of artificial light at night on insectivorous bats

Keywords: Chiroptera, land-cover, landscape-dependant responses, light pollution, outdoor lighting, urbanization

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

Introduction

The loss and fragmentation of habitats constitute two of the main threats to global biodiversity [START_REF] Monastersky | Life -a status report[END_REF]. Therefore, both amount of semi-natural habitats and their spatial organization in landscapes drives species' habitat use and movements at multiple spatial scales [START_REF] Ancillotto | The importance of ponds for the conservation of bats in urban landscapes[END_REF][START_REF] Benton | Farmland biodiversity: Is habitat heterogeneity the key?[END_REF][START_REF] Rybicki | Species-area relationships and extinctions caused by habitat loss and fragmentation[END_REF][START_REF] Sahraoui | Integrating ecological networks modelling in a participatory approach for assessing impacts of planning scenarios on landscape connectivity[END_REF]. For example, farming intensification and urbanization processes are widely recognized as strong pressures negatively affecting biodiversity by decreasing the amount of favourable habitat in landscapes [START_REF] Peng | Landscape configuration and habitat complexity shape arthropod assemblage in urban parks[END_REF][START_REF] Salinas-Ramos | Body Size Variation in Italian Lesser Horseshoe Bats Rhinolophus hipposideros over 147 Years: Exploring the Effects of Climate Change, Urbanization and Geography[END_REF]. Such drivers can act as additive or synergistic pressures on biodiversity [START_REF] Brook | Synergies among extinction drivers under global change[END_REF]. Thus, the magnitude of pressures that affect biodiversity can depend on the heterogeneity in landscape composition or landscape structure (e.g. [START_REF] Caryl | Functional responses of insectivorous bats to increasing housing density support "land-sparing" rather than "land-sharing" urban growth strategies[END_REF][START_REF] Tamburini | Conservation tillage mitigates the negative effect of landscape simplification on biological control[END_REF].

Artificial Light At Night (ALAN) is also an emerging threat to global biodiversity [START_REF] Koen | Emerging threat of the 21st century lightscape to global biodiversity[END_REF][START_REF] Falchi | The new world atlas of artificial night sky brightness[END_REF][START_REF] Kyba | Artificially lit surface of Earth at night increasing in radiance and extent[END_REF]. ALAN affects biological rhythms (Gaston et al., 2017), fitness (e.g. [START_REF] De Jong | Effects of nocturnal illumination on life-history decisions and fitness in two wild songbird species[END_REF], species movements (e.g. [START_REF] Doren | High-intensity urban light installation dramatically alters nocturnal bird migration[END_REF] and has cascade effects on ecosystems [START_REF] Bennie | Artificial light at night causes top-down and bottom-up trophic effects on invertebrate populations[END_REF][START_REF] Knop | Artificial light at night as a new threat to pollination[END_REF]. All these ALAN effects are particularly well-known for insectivorous bats [START_REF] Boldogh | The effects of the illumination of buildings on house-dwelling bats and its conservation consequences[END_REF][START_REF] Mathews | Barriers and benefits: implications of artificial night-lighting for the distribution of common bats in Britain and Ireland[END_REF]Rydell et al., 2017), and ALAN is even proposed as one of the main global threats to bats [START_REF] Voigt | Bats in the Anthropocene : Conservation of Bats in a Changing World[END_REF].

Studies have shown that ALAN reduced bat movements through barrier effects, thus generating habitat fragmentation and loss [START_REF] Hale | The ecological impact of city lighting scenarios: Exploring gap crossing thresholds for urban bats[END_REF]. Such effects can ultimately decrease habitats connectivity for bats within landscape [START_REF] Laforge | Reducing light pollution improves connectivity for bats in urban landscapes[END_REF], connectivity being a key driver of bat activity and populations' health status [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF]. However, how ALAN effects vary across landscape composition, remains little studied. It was recently shown that increasing tree cover 100-500 m around streetlights intensified negative and positive responses of bats to light [START_REF] Straka | Tree cover mediates the effect of artificial light on urban bats[END_REF].

These authors also showed that tree cover can mitigate negative impacts of streetlights on open space foraging bats. Two other studies drew similar conclusions: in case of low tree cover proportion, ALAN had a negative impact on bat activity, while effects tended to be positive under high proportions [START_REF] Mathews | Barriers and benefits: implications of artificial night-lighting for the distribution of common bats in Britain and Ireland[END_REF][START_REF] Pauwels | Accounting for artificial light impact on bat activity for a biodiversity-friendly urban planning[END_REF]. Thus, accounting for spatial variations of ALAN effects according to landscape composition is of high importance to build efficient conservation strategies aiming to limit landscape functionality losses concerning bats.

Two deeply linked pressures can drive bat responses when studying the spatial variation of ALAN effects on insectivorous bats: (i) a decrease in the amount of habitats through their degradation or urbanisation, which in turn reduces food resource for bats (i.e. arthropods; [START_REF] Sánchez-Bayo | Worldwide decline of the entomofauna: A review of its drivers[END_REF], and (ii) the artificial light level with its behaviouralinduced changes (e.g. increased perceived predation risk and flight speed; [START_REF] Jones | Foraging strategy and predation risk as factors influencing emergence time in echolocating bats[END_REF][START_REF] Polak | Differential effects of artificial lighting on flight and foraging behaviour of two sympatric bat species in a desert[END_REF] and its 'vacuum cleaner' effect, which aggregates arthropod preys around light sources while decreasing their amount in unlit areas [START_REF] Eisenbeis | Artificial night lighting and insects: attraction of insects to streetlamps in a rural setting in Germany[END_REF][START_REF] Owens | The impact of artificial light at night on nocturnal insects: A review and synthesis[END_REF]. Thus, bats are potentially constrained by the cost-benefit ratio of foraging at a given place driven by both habitat suitability and artificial light levels. In addition, bats' response to light likely depends on their flight strategy: aerial-hawking species (i.e. from genera such as Eptesicus, Nyctalus and Pipistrellus) appear to be less locally affected by ALAN by being able to exploit insects under light sources, while slow-flying gleaner species (i.e. from genera such as Myotis and Plecotus) avoid streetlights, potentially due to perceived predation risk (Stone et al., 2015a). In addition, one aerial-hawking species, P. pipistrellus, although described as less affected by light at local scales [START_REF] Azam | Is part-night lighting an effective measure to limit the impacts of artificial lighting on bats?[END_REF](Azam et al. , 2018)), was found as negatively affected at regional [START_REF] Pauwels | Accounting for artificial light impact on bat activity for a biodiversity-friendly urban planning[END_REF] and national [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF] scales. We thus specifically expected the global activity (i.e. not differentiating foraging from commuting) of P. pipistrellus to decrease with increasing ALAN.

As a consequence, we hypothesized that global activity of slow-flying gleaner species should decrease with ALAN, and this more strongly in non-urban habitats (areas composed of any land-use types other than impervious surfaces -such as housing, industrial areas or roads -, i.e. farmlands, forests, hedgerows and wetlands). This could be caused by an additive effect between (i) light sources repulsive effects (e.g. related to their intrinsic perception of increased predation risk; [START_REF] Jones | Foraging strategy and predation risk as factors influencing emergence time in echolocating bats[END_REF], and (ii) an increasing prey density in nonurban habitats irrespective of light as insects complete their life cycle on vegetation or in water [START_REF] Lagucki | Urbanization alters communities of flying arthropods in parks and gardens of a medium-sized city[END_REF]. Thus, given that all bat species are expected to be more abundant in areas with more preys, artificial light sources at such places would have stronger repulsive effects on light-shy species such as slow-flying gleaners compared to places less attractive for these species in terms of prey (Fig. 1a). In contrast, we expect that global activity of aerialhawking bat species would be less affected or even positively affected by ALAN, and this more strongly when the amount of non-urban habitats increases, thanks to an additive effect between (i) their ability to exploit insects around light sources (Azam et al., 2018) and (ii) higher insect density in non-urban habitats (Fig. 1b).

Focusing on a study area with a diversified landscape composition (i.e. the amount of each land-use/habitat) along a wide ALAN gradient, we aimed to investigate (i) the speciesspecific response to ALAN at landscape scale using satellite-based radiance as an indicator of artificial lighting for 10 bat species and three bat species groups, and (ii) response variations according to landscape composition around sampling sites (i.e. a few hundred meters around sampling sites). To achieve this goal, we selected sample sites (i) within a gradient of radiance, (ii) while accounting for all possible landscape contexts from the combination of three land-uses (forest, urban, wetland), and (iii) making sure that land-uses were not correlated with each other or with the radiance.

Materials and methods

Study area

We carried out the study over a 33,468 ha area around a highly urbanized city in France:

Valence (Fig. 2). This area exhibits a wide radiance gradient (i.e. a measure of ALAN defined as the radiant flux reflected or emitted by surfaces) from 0 to 94 nW/cm²*sr. Land-use types in the study area were largely agricultural (54%), urban (21%), and forest (21%), while wetlands cover only 4% (Fig. 2; see Environmental variables section for information source).

Note the agriculture land-use in the study area: 86% annual crops (mainly cereals and market gardening), 7% perennial crops (orchard and vineyard) and 7% grasslands, with an average of 20 m of hedgerows per hectare.

Sampling design

The study aimed to investigate the radiance effects on bat activity (i.e. not differentiating foraging from commuting) according to landscape composition. For that we sampled bat activity at 253 sampling sites covering a wide range of radiance (from 0 to 57 nW/cm²*sr used as an indicator of artificial lighting [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF]; Fig. S1, higher than 57 radiance values were too scarce in the study area to be included in the sampling design). Within this radiance range we sought to include a high variability in land-use variables (see Environmental variables section) while minimising correlations between radiance and landuse variables (Figs. S2 &S3; Table S1). Accordingly, we selected sampling sites following the [START_REF] Laforge | Reducing light pollution improves connectivity for bats in urban landscapes[END_REF] approach. We first divided the whole study area into 250x250 m quadrats, whose mean radiance, forest areas proportion, urban areas proportion and distance from the center of quadrats to wetland were calculated. We used these four variables only to select sites, in order to limit the number of possible combinations while maximising variability in landscape contexts. We then categorized each of these variables into four values classes, maximising the inter-class variance and minimising the intra-class variance, using the Jenks Algorithm [START_REF] Jenks | Optimal data classification for choropleth maps Occasional Paper No 2[END_REF]. On the study area, 133 combinations of four classes (i.e. each derived from the radiance, forest proportion, urban proportions or distance to wetlands) were available (see Supporting information S1). Thus, each combination was composed of a given class of radiance, forest proportion, urban proportion and distance to wetland. We randomly selected three quadrats per combination whenever possible (i.e. 253 quadrats selected in total; see Supporting information S1 for details on this quadrat selection method). The selected quadrats central points were defined as the location of sampling sites, or it was slightly moved when inaccessible.

Bat monitoring

Recordings were carried out from the 3 May to 3 July 2017, during 27 nights in the seasonal peak of bat activity according to the French national bat monitoring program "Vigie-Chiro" (http://www.vigienature.fr), under favourable weather conditions: no rain; <5 m/s wind speeds, and > 12°C temperatures. The average nighttime cloud cover varied from 17 to 70 % (47 % in average; see Table S2).

We sampled on average nine sites per night simultaneously, covering a wide range of radiances (Table S2), with at least 750 m between each of them. Bats were recorded throughout the entire night, from 30 min before sunset to 30 min after sunrise, and only once.

Echolocation calls were recorded using one SM4BAT (Wildlife Acoustics Inc., Concord, MA, USA) recorder per site (Supporting Information S2 for details about standardized settings).

Since it is impossible to determine the number of individual bats from their echolocation calls, we calculated an activity metric commonly used in Europe, hereafter named 'bat passes', calculated as the number of bat passes per night [START_REF] Kerbiriou | Bat Pass Duration Measurement: An Indirect Measure of Distance of Detection[END_REF][START_REF] Kerbiriou | Potential of bat pass duration measures for studies of bat activity[END_REF][START_REF] Stahlschmidt | Bats as bioindicators -the need of a standardized method for acoustic bat activity surveys[END_REF]. A bat pass was defined as a single or greater echolocation call separated by at least a 2-second gap without calls, and a maximum duration of 15 seconds.

We did not separated foraging from commuting bat passes. We then automatically identified bat passes to the most accurate taxonomic level possible, using the SonoChiro© software (Biotope, France).

To account for potential errors in automated identifications, we followed the [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] approach proposing a cautious method to ensure results robustness against automated identification errors in acoustic surveys. The method allows, resorting to random manual checking of automated identifications (by combining measurements of energy peak, final frequency, call duration, band width and time between calls, as discussed in Barataud (2015)), to model the error rate for each species or group, according to confidence scores provided by the software (Supporting Information S3, Table S3 and Fig. S4). This method allows presenting results based on a maximum error rate tolerance of 0.5 (MERT), above which data were discarded. This keeps the number of bat passes for all species and species groups great enough for analysis, while limiting false positives. Then results are confirmed on a more restrictive 0.2 MERT, in order to permit conclusive interpretation only when both MERT thresholds are consistent [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF]; see Supporting Information S3, Tables S3 &S4 and Fig. S4 for more details).

Two species groups (i.e. Myotis spp. and Plecotus spp.) were constructed, because species within these groups were difficult to distinguish from each other, based on their echolocation calls [START_REF] Obrist | Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach[END_REF]. We also constructed a third species group, Rhinolophus spp., including Rhinolophus ferrumequinum and R. hipposideros, because of their very low abundances and occurrences (Table S4).

Environmental variables

Since the effects of environmental variables on bat activity can change according to the spatial scale considered and are very context-dependent and difficult to predict, we calculated their metrics regarding average, proportion or length at five different buffer sizes used in previous studies (200, 400, 600, 800 and 1000 m;[START_REF] Kalda | Multi-scale ecology of woodland bat the role of species pool, landscape complexity and stand structure[END_REF][START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF], using the QGIS software.

For each sampling site and each buffer size, we first extracted the average radiance in buffers from VIIRS night-time lights. Then, to characterize landscape composition, we computed 11 land-use variables (either distances or length/proportion in buffers) varying across sites (Table S5), which are well-known good bat activity predictors: the proportion of, and distance to urban (e.g. [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF], forest (e.g. [START_REF] Boughey | Effects of landscape-scale broadleaved woodland configuration and extent on roost location for six bat species across the UK[END_REF], farmland (e.g. [START_REF] Roeleke | Habitat use of bats in relation to wind turbines revealed by GPS tracking[END_REF] and wetland areas (e.g. [START_REF] Amorim | Following the water? Landscape-scale temporal changes in bat spatial distribution in relation to Mediterranean summer drought[END_REF][START_REF] De Conno | Testing the performance of bats as indicators of riverine ecosystem quality[END_REF]; hedgerow length (e.g. [START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF][START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF], road (e.g. [START_REF] Berthinussen | The effect of a major road on bat activity and diversity[END_REF] and wooded edges [START_REF] Heim | The relevance of vegetation structures and small water bodies for bats foraging above farmland[END_REF].

Radiance was extracted from VIIRS night-time lights, which is a yearly and cloud-free composite raster (2016) with a 450-m pixel size produced by the Earth Observation Group and NOAA National Centres for Environmental Information (NCEI) (https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html). Environmental variables (2016) were provided by the National Institute of Geography in a vectorial shapefile with a 2.5-m spatial accuracy (from BD TOPO for data on forests, hedgerows and urban areas, from BD Carthage for wetland data, from BD ORTHO and Graphical Parcel Register for farmland data).

Statistical analyses

We examined the radiance effects on bat activity according to land-use variables around sampling sites (i.e. landscape composition). A summary of the statistical analysis steps presented below is shown in Figure S5. We ran Generalized Linear Mixed Models (GLMMs, R package lme4; [START_REF] Bates | Fitting Linear Mixed-Effects Models Using {lme4}[END_REF] for each species or species group separately, using the number of bat passes per site as response variable. We used a negative binomial error distribution to keep low overdispersion ratios in models (<1.50; Table S5; [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF].

For two rare taxa (Barbastella barbastellus, Rhinolophus spp.; Table S3), information relative to bat activity variation were too scarce, thus we used the presence-absence per site as response variable associated with a binomial error distribution. To build full models, we included the radiance and land-use variables as fixed effects. In order to assess the dependence on landscape composition of potential radiance effects on bat activity, we included all possible simple interactions between radiance and land-use variables. All variables used as fixed effects were scaled (i.e. by subtracting the mean and dividing by the standard deviation). To control for inter-night variation in bat activity (e.g. due to weather conditions) and according to our sampling design based on simultaneous recordings each night along a radiance-landscape gradient, we included the date as random effect in models.

For each variable included in the full models presented above, except where distances are concerned, we selected the best buffer size for each species. To do this, we ran one univariate model per variable per buffer size for each species, and selected the one with the smallest Akaïke Information Criteria (AIC). Thus, each full model can contain different optimal buffer sizes between variables (Tables S6 &S7). Full models were constructed by excluding any explanatory variable responsible for multicollinearity with a < 2 variance inflation factor (VIF) value using the vif function (R package car; Fox and Weisberg, 2019; see Supporting information S4 and table S6 for full models composition). Thus, removing variables responsible for multicollinearity restrained the number of predictors (ranging from 8 to 17) in full models, depending on species (Table S6). We also assessed potential non-linear effects for all variables by visual inspection of plots from Generalized Additive Mixed Models (GAMM, R package mgcv; [START_REF] Wood | Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models[END_REF]. Seven quadratic relationships were observed for B. barbastellus, Miniopterus schreibersii, Myotis spp., Pipistrellus kuhlii, Pipistrellus nathusii and Plecotus spp. To account for these non-linear relationships, we added quadratic effects on these variables in full GLMMs (Table S6). We also checked for potential spatial autocorrelation in model residuals using the Moran.I function to test significance (R package spatial; [START_REF] Venables | Modern Applied Statistics with S, Fourth[END_REF]. We found significant spatial autocorrelation for Eptesicus serotinus, Myotis spp., Nyctalus leisleri, Nyctalus noctula and P. pipistrellus. To the full models, we added a distance-weighted variable in addition to other explanatory variables, to account for spatial autocorrelation (Table S6; [START_REF] Bardos | Valid auto-models for spatially autocorrelated occupancy and abundance data[END_REF], using the autocov_dist function (R package spdep; [START_REF] Bivand | Comparing implementations of global and local indicators of spatial association[END_REF].

Then, following a multi-model inference approach [START_REF] Grueber | Multimodel inference in ecology and evolution: Challenges and solutions[END_REF], we generatedfrom the full model for each species and species groupsa set of candidate models containing all possible variable combinations ranked by corrected AIC (AICc). Using all models with a delta AICc < 2 (and a delta AICc < 6 for comparison; [START_REF] Grueber | Multimodel inference in ecology and evolution: Challenges and solutions[END_REF], we averaged regression coefficients for each fixed effect, using the model.avg function (R package MuMIn; [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF]. Fixed effects were averaged only over models in which that fixed effect appeared (i.e. the so-called natural average method; [START_REF] Grueber | Multimodel inference in ecology and evolution: Challenges and solutions[END_REF]. From the model averaging procedure, we were also able to extract the relative importance of each variable (estimated by summing the weights of models in which the variable appeared) and the percentage of explained variance by full and best models (i.e. r squared), using the rsquared function (R package piecewise SEM; [START_REF] Lefcheck | piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics[END_REF]. As it is not possible to run the rsquared function on averaged models, we computed it for best and full models in order to provide a global view of the goodness-of-fit.

All analyses were performed in the R software v.3.5.1 (R Core Team, 2020).

Results

Bat monitoring

At the 253 sampling sites, we recorded 79,661 bat passes in total, including 10 species and three species groups, where the most abundant species was P. kuhlii, representing 71% of passes and present in 100% of sites (Table S3). The least common species were E. serotinus (43 passes), Rhinolophus spp. (32 passes) and B. barbastellus (208 passes), which were recorded respectively in 9%, 13% and 5% of sites (Table S3).

Radiance effects on bat activity

The radiance variable had a significant effect on the activity of five species or species groups (Table 1), with high relative importance (Table S8), and these were selected in all top candidate models with a < 2 AICc delta for nine species or species groups, and in a minimum of 50% of top candidate models for the four remaining ones. We detected significant decrease in activity of Myotis spp., Plecotus spp. and P. pipistrellus due to radiance (Table 1; Fig. 3).

We also detected significant increase in activity of P. kuhlii and P. pygmaeus due to radiance (Table 1; Fig. 3).

Interaction between radiance and landscape composition

Depending on species, best models (i.e. with the lowest AICc) explained a percentage of variance ranging between 7% and 34%, except for those from E. serotinus and N. noctula, which explained very low variance (i.e. 1 and <0.1 %, respectively; Table S6).

We found a significant interaction effect between radiance and land-use variables for 8 species or species groups (Table 1). The results of these interactions as a whole show that the positive or negative effects of light on bats, depending on species, mainly occurred in nonurban habitats. Specifically, bat activity becomes increasingly higher under high radiances with the increasing proportion of farmland areas for P. pygmaeus, while for N. noctula activity it is higher under low radiances with low proportions of farmland; and higher under high radiances with high farmland proportions (Table 1; Fig. 4). We found a similar result concerning H. savii and P. nathusii, whose activity increased with increasing radiance only when proportion of forests became high (Table 1; Fig. 4). However, we found higher activity of N. leisleri under high radiances far from forests (Table 1; Fig. 4). We also found higher H. savii activity under high radiances far from wetlands, while M. Schreibersii was positively affected by radiance below 700 m from wetlands and negatively affected above (Table 1; Fig. 4). We found the opposed pattern for N. noctula and N. leisleri, whose activity became higher under high radiances along with the increasing proportion of wetlands, although sampled gradients were less extensive (Table 1; Fig. 4).

Concerning urban variables, we found an increasing M. schreibersii activity with increasing radiance, which mainly occurred in non-urban habitats (Table 1; Fig. 4). We found the same pattern for Myotis spp., for which the decrease in activity due to radiance became much higher with increasing distance to urban areas, as well as the increase in P. kuhlii activity due to radiance, which became increasingly higher with increasing distance to urban areas (Table 1; Fig. 4). For these three species or group, radiance was positively associated with the increasing proportion of, or the decreasing distance to urban areas (Fig. S6), even though this did not result in any collinearity issue.

Results were qualitatively very similar and little changes were found after we re-ran analyses at the 0.2 maximum error rate threshold (see Table S9). However, two new significances were found for P. pipistrellus. Specifically, we found an increasingly negative effect of radiance on P. pipistrellus activity, with an increasing length of hedgerows and a decreasing distance to wetlands (Table S9;Figs. 4 & 5). Finally, averaging candidate models in a delta AICc < 6 instead of 2 did not change results (Table S10).

We show that for 9 bat taxa out of the 13 tested, ALAN affects bat activity (i.e. not differentiating foraging from commuting) especially in non-urban habitats. We also show that, as expected, the responses depend on the species. For five species, the intensity of ALAN (i.e. apart from its interactions with landscape variables) increased the activity of P. kuhlii and P. pygmaeus and decreased the activity of Myotis spp., Plecotus spp. and P. pipistrellus, and these effects were significantly driven by landscape composition. More notably, for five species, the ALAN effects on activity were only significant in particular landscape compositions (H. savii, M. schreibersii, N. leisleri, N. noctula and P. nathusii), which make such effects undetectable without exploring ALAN-landscape composition interactions. Specifically, both positive or negative ALAN effects on bat activity occur much more often in non-urban habitats. All these results highlight (i) the need to prioritize ALAN reduction efforts in non-urban habitats, (ii) how important is to account for landscape composition when studying ALAN effects on bats to avoid missing effects, and (iii) the need to consider both ALAN and landscape composition when designing and implementing green infrastructures (i.e. strategically planned European network of natural and semi-natural areas) (European Commission 2009).

Radiance effects on bat activity

Overall responses of bat species to radiance were consistent with the literature for slowflying/gleaner species (i.e. Myotis and Plecotus species) widely known to be negatively impacted by artificial lighting (e.g. Azam et al., 2018;[START_REF] Lacoeuilhe | The influence of low intensities of light pollution on bat communities in a semi-natural context[END_REF]. Possible explanations could be the relative high sensitivity of the bat eye to the blue part of light spectrum [START_REF] Müller | Bat eyes have ultraviolet-sensitive cone photoreceptors[END_REF], the increase in abundance of less light-shy species at the expense of slow-flying gleaner species [START_REF] Arlettaz | Competition for food by expanding pipistrelle bat populations (Pipistrellus pipistrellus) might contribute to the decline of lesser horseshoe bats (Rhinolophus hipposideros)[END_REF], or the increased predation risk due to their slow flight [START_REF] Jones | Foraging strategy and predation risk as factors influencing emergence time in echolocating bats[END_REF]. Positive effects of radiance detected for P. kuhlii and P. pygmaeus (i.e. the aerial-hawking species guild) were consistent with literature based on local measures of ALAN (i.e. a street lamp level, e.g. [START_REF] Azam | Is part-night lighting an effective measure to limit the impacts of artificial lighting on bats?[END_REF], likely due to increased insect density close to lights (Rydell, 1992). However, our results were opposed to those found by [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF] carried out at national scale. The negative effect detected on P. pipistrellus was consistent with studies carried out at national [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF] or agglomeration scales [START_REF] Pauwels | Accounting for artificial light impact on bat activity for a biodiversity-friendly urban planning[END_REF]. However, positive effects on P.

Pipistrellus species are also reported at street light scale (e.g. Azam et al., 2018). Such opposed pattern could be explained by the scale effect and light variable used: (i) studies carried out at local scale involved ground-based data measurements at very small scales (a few meters) and focus on street light contexts, highlighting likely attractiveness of streetlight lamp for insects and, in turn, a number of bats, while (ii) studies carried out on a large scale (countries or cities) have more chances to involve more sampling of unlit areas close to and far from lit ones, which allows for detecting insects depletion due to the "vacuum cleaner" effect in unlit areas close to lit ones [START_REF] Eisenbeis | Artificial night lighting and insects: attraction of insects to streetlamps in a rural setting in Germany[END_REF][START_REF] Owens | The impact of artificial light at night on nocturnal insects: A review and synthesis[END_REF].

Interaction between radiance and landscape composition

First, we found that radiance effects on bat vary among landscape compositions in batfriendly non-urban habitats (i.e. farmland -including non-negligible amount of perennial crops and grasslands in the study area-, forest, hedgerow and wetland). Radiance had higher effects on bat activity with increasing non-urban habitats (i.e. an increasing amount of, or proximity to, land-uses other than impervious surfaces, or a decreasing amount of or proximity to urban areas). These results are in accordance with a recent study conducted at agglomeration scale, which showed that dense tree cover amplified positive or negative effects of street light for P. pipistrellus, P. pygmaeus and Myotis spp. [START_REF] Straka | Tree cover mediates the effect of artificial light on urban bats[END_REF].

Two other studies showed that high tree cover mitigated the negative effects of streetlights on bats [START_REF] Mathews | Barriers and benefits: implications of artificial night-lighting for the distribution of common bats in Britain and Ireland[END_REF], Pauwels et al., 2019). One proposed explanation for positive effects is that tree cover could reduce the predation risk linked to light and could also, combined with streetlights, attract more insects and therefore constitute advantageous foraging grounds. Alternatively, close to trees, flying bats could also benefit from favourable microclimate or more structured flightpaths, which offset lighting-generated disadvantages. In our study, we found such mitigating of radiance effects only from increasing proportion of farmland areas and from decreasing distance to wetlands, for N. noctula, and M. schreibersii, respectively.

For species found to be negatively impacted by radiance in our study, we found P. pipistrellus to be more impacted at sites with highest length of hedgerows and closest to wetlands, and Myotis spp. to be more impacted at highest distances to urban. As hedgerows constitute a key landscape element for bat commuting (Pinaud et al., 2018), lighting at such linear landscape elements may disconnect foraging habitat and even limit their accessibility for bats [START_REF] Laforge | Reducing light pollution improves connectivity for bats in urban landscapes[END_REF]. In addition, we should be cautious about results for P. pipistrellus and confirm them in further studies, given that these were only significant at the most restrictive threshold of maximum error rate tolerance (MERT) in acoustic data [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF]. Wetlands also play an important role for foraging and for drinking for most bat species [START_REF] De Conno | Testing the performance of bats as indicators of riverine ecosystem quality[END_REF][START_REF] Roeleke | Habitat use of bats in relation to wind turbines revealed by GPS tracking[END_REF], and light sources close to wetlands particularly attract aquatic insects [START_REF] Manfrin | Artificial Light at Night Affects Organism Flux across Ecosystem Boundaries and Drives Community Structure in the Recipient Ecosystem[END_REF] and in turn potentially induce an insects depletion around due to the "vacuum cleaner" effect. Thus, negative effects of ALAN on slow-flying gleaner species would be stronger close to wetlands. Finally, given that an increasing proportion of urban areas reduce the amount and complexity of available vegetation and in turn of arthropod food resource [START_REF] Sánchez-Bayo | Worldwide decline of the entomofauna: A review of its drivers[END_REF], and since insectivorous bat species strongly depend of high levels of vegetation complexity [START_REF] Suarez-Rubio | Insectivorous bats respond to vegetation complexity in urban green spaces[END_REF] and arthropods amounts [START_REF] Charbonnier | Numerical and functional responses of forest bats to a major insect pest in pine plantations[END_REF], it is not surprising to find mainly higher negative impacts of radiance in non-urban habitats. [START_REF] Laforge | Reducing light pollution improves connectivity for bats in urban landscapes[END_REF] draw similar conclusions showing that lighting reduction was more efficient to restore bat presence in vegetated areas than in cities.

P. pygmaeus activity increase with illumination, especially on farmland. Despite a sampling design elaborated for minimising correlations between land-use variables, we observed an opposition between urban and farmland cover in our study sites (correlation coefficient: -0.59; Table S8). Thus, found effects of radiance according to the farmland variable could mirror that non-urbanized areas are more attractive than urbanized areas (Border et al., 2017).

Further studies remain needed to investigate why P. pygmaeus is especially active in lit more than dark farmland. H. savii and P. nathusii were also found to be more affected at sites with high forest proportions. Forest edges can drive activity and foraging of P. nathusii more than other habitats as hedgerows [START_REF] Heim | The relevance of vegetation structures and small water bodies for bats foraging above farmland[END_REF], while, to our knowledge, no similar studies have tested the same for H. savii. We hypothesize that the interest of forest edges coupled with lighting attracting arthropod prey [START_REF] Owens | The impact of artificial light at night on nocturnal insects: A review and synthesis[END_REF] could enhance the attractiveness of such habitat for these species. We also found M. schreibersii to be more affected by radiance at the lowest urbanized sites, and P. kuhlii to be increasingly affected with increasing distance to urban. Such a result is also consistent with the hypothesis we discussed above for aerial-hawking flying species: increasing amount of urban areas leads to a decreasing amount of other land-uses, which in turn reduces arthropod prey availability for bats, artificial light attractivity being therefore less efficient in such a landscape.

Limitations and perspectives

Most effects found were robust against error rates in acoustic data and the presented interactions did not suffer from any confounding effects between radiance and land-use variables. However, results validity could not be checked at the 0.2 MERT threshold for E. serotinus, N. noctula and P. nathusii, potentially because of too low remaining numbers of bat passes, given that such thresholds discarded a lot of data (Table S11; Supporting information S5).

Satellite-based data used in this study were calculated using cloud-free images, which potentially underestimate impacts for weather conditions promoting a skyglow caused by upwardly emitted artificial light being scattered in the atmosphere [START_REF] Kyba | Worldwide variations in artificial skyglow[END_REF]. Our bat monitoring was carried out under non-negligible cloud covers which varied from 17 to 70 % (47 % in average; Table S2) over the 27 nights studied, which could significantly increase skyglow. Since a skyglow can be comparable to late twilight and moonlight [START_REF] Gaston | Impacts of Artificial Light at Night on Biological Timings[END_REF], we could expect it to affect the use of space by bats [START_REF] Roeleke | Aerial-hawking bats adjust their use of space to the lunar cycle[END_REF] and in turn the ALAN effects-landscape relationship. Further studies could thus assess how the skyglow, and more generally less favourable weather conditions, would impact results we report in this study. In addition, further studies should compare such results from the VIIRS raster with ones from more local light intensity measurements to study finer-scale factors, as performed in [START_REF] Straka | Tree cover mediates the effect of artificial light on urban bats[END_REF], [START_REF] Pauwels | Accounting for artificial light impact on bat activity for a biodiversity-friendly urban planning[END_REF] and [START_REF] Hale | The ecological impact of city lighting scenarios: Exploring gap crossing thresholds for urban bats[END_REF]. Indeed, given that our explanatory variable was a radiance pixels averaging in a given radius around sites, there is undeniably an inaccuracy in the real distances of impact. Accurate distances of impact are nevertheless essential for concrete recommendations in local lighting management. In addition, although land-uses used in this study to describe landscape composition were the most accurate information we had, future studies could assess ALAN effects on bats according to landscape composition using (i) more accurate spatial resolution of ALAN data and (ii) finer habitat descriptors, for example by differentiating different types of farming in the farmland category, or by measuring structure of woody habitats.

Although we found some positive effects of radiance, artificial lighting is expected to induce negative effects on a larger scale [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF], likely due to prey accumulation and 'vacuum cleaner effect' around light sources decreasing prey availability on larger scales [START_REF] Eisenbeis | Artificial night lighting and insects: attraction of insects to streetlamps in a rural setting in Germany[END_REF][START_REF] Owens | The impact of artificial light at night on nocturnal insects: A review and synthesis[END_REF]. As a consequence, positive relationships at local scale between bat activity and ALAN should be interpreted with caution. Finally, one group (Plecotus spp.) remains strongly negatively affected by ALAN effects irrespective of landscape composition, which in turn involves thinking of reduction possibilities everywhere for this group.

Conclusions

Artificial light effects on bats were predicted to vary according to landscape composition, but had so far not received attention at a regional scale for a large number of landscape predictors and bat taxa. We show that the magnitude of most ALAN effects on bats is driven by landscape composition. Some ALAN effects were even only detectable in particular landscape compositions, making the main effect of ALAN undetectable without account for interactions with landscape. This underpins the great importance to prioritize ALAN reduction schemes for bat conservation in non-urban habitats, and how important is to account for landscape composition when studying ALAN effects on bats to avoid missing effects. Indeed, most found effects occurred in non-urban habitats, thereby highlighting the importance of minimizing lighting close to these areas.

Figure 1 .

 1 Figure 1. Shematic representation of the tested hypothesis showing the expected variation of artificial light effects on bat activity according to landscape composition for slow-flying gleaner (a; e.g. Myotis and Plecotus groups) and aerial-hawking (b; e.g. Eptesicus, Nyctalus and Pipistrellus species) bats. The direction of the grey arrows shows the expected ways of increasing bat activity in term of light-landscape combinations, and the grey arrows width shows expected the magnitude of changes.

Figure 2 .

 2 Figure 2. Map of the study area in France and the 253 sampling sites, according to the main land-use (a) and radiance gradient (b).

Figure 3 .

 3 Figure 3. Relation between the predicted number of bat passes per night and radiance values predicted from best models for species or species groups significantly affected by the radiance variable alone (i.e. a global effect apart from its interactions with landscape variables), and associated 95% confidence intervals. Circles show each of the 253 recording sites.

Figure 4 .

 4 Figure 4. Predicted number of bat passes from GLMMs according to the interaction between radiance and land-use variables computed as (A) proportions and (B) distances. The colour scale represents the predicted mean number of bat passes per night, darker colours show higher number of bat passes. Circles represent each of the 253 combinations between radiance and land-use values sampled in the study. Predictions were restricted to the maximum convex polygon of sampled radiance-landscape variables combinations, and white surfaces show uncovered gradients. For each plot, the bat guild (i.e. aerial-hawing or slow-flying gleaner species) and the direction of bat response to radiance are shown with acronyms and symbols.

Figure 5 .

 5 Figure 5. Predicted number of aerial-hawking species passes from GLMMs according to the radiance in interaction with (A) the length of hedgerows in a 800m radius for P. pipistrellus and (B) the distance to hedgerow for N. leisleri. The colour scale represents the predicted mean number of bat passes per night, darker colours show higher number of bat passes. Circles represent each of the 253 combinations between radiance and land-use values sampled in the study. Predictions were restricted to the maximum convex polygon of sampled radiance-landscape variables combinations, and white surfaces show uncovered gradients.Both plots show a negative effect of light on bat taxa.

Table 1 .

 1 Averaged estimates, standard errors in parentheses and significance levels (*** P < 0.001, **

P < 0.01, * P < 0.05, . P < 0.1) from the set of best candidate models with an AICc delta < 2 for each

species (Barbar: Barbastella barbastellus; Eptser: Eptesicus serotinus; Hypsav: Hypsugo savii;

Minsch: Miniopterus schreibersii; Myosp: Myotis spp.; Nyclei: Nyctalus leisleri; Nycnoc: Nyctalus

noctula; Pipkuh: Pipistrellus kuhlii; Pipnat: Pipistrellus nathusii; Pippip: Pipistrellus pipistrellus;

Acknowledgements

This work was supported by the French land-transport infrastructure, ecosystems and landscapes (ITTECOP) program, including funding sources from the Environment and Energy Management Agency (ADEME) and the Foundation for Research on Biodiversity (FRB), as well as the LabEx DRIIHM (ANR-11-LABX-0010). We especially thank the Auvergne-Rhône-Alpes region for funding data collection fees. We are also grateful for Léa Mariton help with proofreading.

Author's contribution KB conceived the ideas; KB, AV, AD and SV designed the methodology; AD and AV collected the data; AD, AV and TD manually checked bat passes; KB and AV analysed the data; KB with the support of CK, CA, AV and IL led writing of the manuscript. All authors critically contributed to the drafts and gave their final approval for publication.

Data accessibility

Dataset used will be available in a dedicated platform. Lighting gradient


MD Elise Ouedraogo

MD Lucie Allard

MD Hélène Bihan

Jeanne Goupil De Bouillé

Bénédicte Giroux-Leprieur

Angela Sutton

Constant Josse Camille Baudry

MD Morgane Didier

David Deutsch

MD Jeanne Goupil De Bouille

PhD Imen Rezgani

MD Olivier Bouchaud

Prof Emmanuel Cosson
email: emmanuel.cosson@aphp.fr

MD B Giroux Leprieur

Sutton Phd

C Josse

The association of metabolic syndrome and COVID-19 deterioration

Keywords: Coronavirus Disease 2019 COVID-19 deterioration, metabolic syndrome, prevalence, prognosis

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Coronavirus Disease 2019 (COVID-19) first appeared as a contagious respiratory viral emerging pathology in Hubei region in December 2019. In the subsequent 3 months, it grew into a pandemic and has not been stopped yet even a global vaccine policy. By July 2021, more than 181 million people had been infected, with over 3,9 million deaths [START_REF] Cegolon | Hypothesis to explain the severe form of COVID-19 in Northern Italy[END_REF]. Severe forms of COVID-19 occur in 15% of patients (2). Many risk factors of severe infection have been identified including male gender (3), advanced age (4) and cardiovascular risk factors [START_REF] Huang | Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia -A systematic review, meta-analysis, and meta-regression[END_REF] such as hyperglycemia [START_REF] Huang | Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia -A systematic review, meta-analysis, and meta-regression[END_REF], hypertension [START_REF] Kulkarni | COVID-19 and hypertension[END_REF] and lipid disorders [START_REF] Hu | Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection[END_REF]. Furthermore, obesity seems to be predictive of COVID-19 deterioration: patients with grade 2 obesity are seven times more likely to be admitted to an intensive care unit (ICU) than patients with lean body weight [START_REF] Tamara | Obesity as a predictor for a poor prognosis of COVID-19: A systematic review[END_REF]. In one study, the prevalence of mechanical ventilation in ICU was 81% for patients with obesity compared with 41% for patients with lean body weight [START_REF] Simonnet | High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation[END_REF].

There are different phenotypes of obesity. Android obesity might be a key prognostic factor of severe COVID-19 through inflammatory mechanisms [START_REF] Chiappetta | COVID-19 and the role of chronic inflammation in patients with obesity[END_REF].

Metabolic syndrome (MetS) is defined by the coexistence of between 3 and 5 of the following components: android obesity, high glucose level, hypertension, hypertriglyceridaemia and low levels of HDL cholesterol [START_REF] Huang | A comprehensive definition for metabolic syndrome[END_REF]. In this context, we hypothesized that MetS diagnosis (as per this definition but using HbA1c to define hyperglycaemia) is associated with deterioration of patients admitted to non-ICUs with COVID-19, and that the higher the number of components, the more frequent the deterioration of COVID-19 infection. The poor prognosis of metabolic syndrome, defined by obesity instead of high waist circumference and diabetes instead of fasting hyperglycemia, was recently described [START_REF] Xie | Metabolic Syndrome and COVID-19 Mortality Among Adult Black Patients in New Orleans[END_REF][START_REF] Lohia | Metabolic syndrome and clinical outcomes in patients infected with COVID-19: Does age, sex, and race of the patient with metabolic syndrome matter?[END_REF]. The aim of our study was to evaluate in patients admitted to four COVID-19 hospital departments (excluding ICU), (i) the prevalence of MetS and (ii) whether MetS is associated with COVID-19 deterioration.

Methods

Study design, setting and participants

We conducted a retrospective observational study in Avicenne Hospital, Bobigny, France. All consecutive patients admitted between April 9 and May 29, 2020 and between February 1 and March 26, 2021, in one of the hospital's four in-patient's departments receiving COVID-19infected patients directly from the emergency room, were included. COVID-19 was confirmed by nasal polymerase chain reaction (PCR). Non-inclusion criteria were (i) pregnancy and (ii) ineligibility to be subsequently transferred to an ICU in case of disease deterioration.

In Avicenne hospital, all patients (COVID or not) are informed at admission that their medical records could be used for research, unless they indicate their opposition. For the present study, no patient indicated any opposition. Approval for the use of patient data was provided by the local ethics committee (approval number: CLEA-2020-121). Data were analyzed anonymously.

Data collection

Data were extracted from patients' medical files and collected in a secure health database.

General data included routine prescribed treatments, health insurance coverage, current tobacco consumption and ethnicity.

Medical history data including diabetes, hypertension and dyslipidemia were self-reported or inferred from blood glucose, blood pressure or lipid-lowering agents, respectively. Cancer, chronic bronchopneumopathy and Human Immunodeficiency Virus (HIV) infection were self-reported or recorded from patients' medical files. Additionally, we collected data for any comorbid conditions to calculate the Charlson Index ( 14) and any history of sleep apnea, nonalcoholic steatosis hepatitis. Obesity was defined by body mass index (BMI) ≥ 30 kg/m². Weight and height were measured within 24 hours following emergency unit admission.

Chest computed tomography scan data included the extent of typical COVID-19 lesions was classified into two groups: minimal to moderate (<25%) and extended to critical (≥25%).

Treatment received during hospitalization was collected.

Biomarkers data included (i) metabolic biomarkers as fasting plasma glucose, HDL cholesterol, triglycerides, HbA1c (ii) inflammation biomarkers, C reactive protein (CRP) and fibrinogen. All these biomarkers were routinely measured on plasma from fasting individuals.

A Cobas 6000 analyzer (Roche diagnostics) was used for all biomarkers except fibrinogen (chronometry -Thrombin Dade), where a Siemens analyzer was used. SARS-CoV-2 RT-PCR was performed on naso-pharyngeal samples collected in virus transport medium, using the SARS-CoV-2 Amp Kit on the automated device Alinity strictly following the recommendations from the manufacturer.

Metabolic syndrome

We adapted the National cholesterol education program adult treatment panel III to define MetS [START_REF] Huang | A comprehensive definition for metabolic syndrome[END_REF]. Because fasting glycaemia values can be higher during infection, we decided to define MetS replacing the component 'fasting glycaemia ≥ 6 mmol/L' with 'HbA1c ≥ 5.7%' which reflects hyperglycaemia independently of stress. More specifically, patients meeting at least three of the following five MetS components [START_REF] Huang | A comprehensive definition for metabolic syndrome[END_REF] were categorized as having the syndrome: (i) android obesity (i.e., waist circumference measured with a plastic anthropometric tape placed at half distance between the costal edge and the iliac crest, in the standing position, exceeding 88 cm for women and 102 cm for men), (ii) HbA1c ≥ 5.7% or pharmacologic treatment or already diagnosed diabetes, (iii) medical history of hypertension or evidence of a repeated measurements of high blood pressures leading to introduction of antihypertensive medication during hospitalization, (iv) triglycerides level ≥ 1.6 mmol/L or fibrate usual medication, and (v) HDL-cholesterol level < 1 mmol/L in males or < 1.6 mmol/L in females or routine prescribed fibrate medication. We tallied the number of MetS components in patients for whom the status for all five components was known.

We made a sensitivity analysis defining metabolic syndrome using fasting hyperglycemia instead of Hb1ac: MetSFPG (hereafter called).

Definition of COVID-19 deterioration and associated standard of care

COVID-19 deterioration was defined as the need for nasal oxygen flow at or above 6 liters per minute during hospitalization within 28 days after admission. We also considered noninvasive ventilation requirement as a secondary outcome.

International standards of care were followed according to a local guideline which was updated as necessary on a daily basis.

Statistical analyses

For power calculation, we estimated that 40% of the inpatients in the hospital's four COVID-19 departments (i.e., excluding the emergency unit and ICU) would be diagnosed with MetS.

We presumed that 30% of the MetS group would have COVID-19 deterioration vs 10% of the no-MetS group. Accordingly, we calculated that a minimum study population of 120 patients would be needed to show a significant difference in the rate of COVID-19 deterioration between the two groups (alpha risk of 5% and power of 80%).

Baseline continuous variables were expressed as mean ± standard deviation. Categorical variables were expressed as frequencies (percentages). Missing data were not replaced. The Kruskal-Wallis test was used to compare continuous variables, while Pearson's chi-squared (X 2 ) test or the Fisher's exact test were used (depending on the number of patients within each group) for categorical variables. The correlation between the number of MetS components and COVID-19 deterioration was assessed thanks to exact Cochran-Armitage test for trend.

The associations between MetS and COVID-19 deterioration (model 1), and between MetSFPG (sensitivity analysis) and COVID-19 deterioration (model 2) were computed using logistic regression, taking into account the following variables: age, gender, ethnicity, period of inclusion and Charlson Index. All tests were two-sided and used a significance level p-value of 0.05. Analyses were conducted with SAS 9.4 software.

Results

Study population characteristics

A total of 223 patients were admitted to the four hospital units after being transferred from the emergency department. Twenty-two of these were excluded because they were not eligible to be subsequently transferred to ICU in case of deterioration. A further 17 were excluded because any positive COVID-19 PCR was available. Finally, we could not characterize a possible MetS for 29 patients. This was because, all patients combined, we did not have enough information for all MetS components. More specifically, a patient could not be classified (i.e., MetS or no-MetS) if s/he did not have data for at least 3 components. Therefore, 155 patients (i.e., the study population) were included (Flow chart in Additional Figure 1).

Prevalence of MetS and MetSFPG

Seventy-six patients (46.6%) and 118 (77.6%) patients were obese and overweight/obese (BMI ≥ 25 kg/m²), respectively, while 126 (81.3%) were defined as having MetS. As shown in Table 2, 102 (72.3%) patients had android obesity, 135 (89.4%) had a high HbA1c level, 112 (73.7%) fasting hyperglycemia, 93 (60.0%) high blood pressure, 66 (43.7%) high triglycerides levels, and 128 (85.9%) low HDL-cholesterol levels. The specific characteristics for each MetS component are shown in Table 2. The prevalence of MetSFPG was 78.8%.

Parameters associated with COVID-19 deterioration

MetS was associated with COVID-19 deterioration (no MetS vs MetS, 13.8% vs 41.3% respectively, p<0.01, Figure 1) and with non-invasive ventilation (3.1% vs 19.1% respectively, p=0.05). MetSFPG was also associated with COVID-19 deterioration (no MetSFPG vs MetSFPG, 15.6% vs 42.0% respectively, p<0.01) and with non-invasive ventilation (3.1% vs 19.3% respectively, p=0.01). High HbA1c (p=0.04) (and fasting hyperglycemia, p=0.02) and high triglycerides level (p=0.04) were the only MetS components associated with COVID-19 deterioration when analyzed individually (Table 2).

Patients who experienced COVID-19 deterioration were older, more likely to be male (Table 1).

Logistic regression accounting for MetS, age, gender, ethnicity, period of inclusion and

Charlson Index showed that people in the MetS group were 5.3 times more likely to have COVID-19 deterioration (95% confidence interval 1.3-20.2) than those in the no-MetS group (Table 3). Logistic regression accounting for MetSFPG, gender, ethnicity, period of inclusion and Charlson Index showed that people in the MetSFPG group were 5.2 times more likely to have COVID-19 deterioration (95% confidence interval 1.5-18.4) than those in the no-MetSFPG group (Table 3) Data for all five components of MetS were available for 133 patients. The higher the number of components, the more frequent the deterioration of COVID-19 infection (p trend ≤0.01) (Figure 1).

Discussion

The main results of our observational study in a multiethnic population admitted to the four COVID-19 departments (excluding ICU) in Avicenne Hospital after transfer from the hospital's emergency department were: (i) the very high prevalence of MetS (>80%) and (ii) the fact that patients with MetS were five times more likely to have COVID-19 deterioration than patients without MetS, after multivariable adjustment. Our results also suggest that the higher the number of MetS components, the more frequent the deterioration of the infection.

Accordingly, MetS should be considered a composite predictor of COVID-19 deterioration.

Two studies from United States, which instead of waist circumference considered BMI to measure the obesity component, reported a prevalence of MetS of 66% in 287 hospitalized COVID-19 patients in New Orleans, LA (12) and of 30.6% in 1871 patients [START_REF] Lohia | Metabolic syndrome and clinical outcomes in patients infected with COVID-19: Does age, sex, and race of the patient with metabolic syndrome matter?[END_REF]. Another study, which took place in a ICU in Teheran, Iran, reported a prevalence of MetS of 47.1% in 157 patients [START_REF] Alamdari | The impact of metabolic syndrome on morbidity and mortality among intensive care unit admitted COVID-19 patients[END_REF].

In our study sample, more than 14.2% of patients met all five components of MetS. The prevalences of each component (i.e., taken individually) have not been studied equally in the literature for COVID-19 patients. In studies in Europe and the United States, pre-diagnosed type 2 diabetes prevalence in patients admitted to COVID-19 units (excluding ICU) was between 30 and 60% [START_REF] Singh | Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations[END_REF]. The few available data on fasting hyperglycaemia suggest a prevalence of between 50% and 62% [START_REF] Liu | Fasting blood glucose predicts the occurrence of critical illness in COVID-19 patients: A multicenter retrospective cohort study[END_REF][START_REF] Wu | Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study[END_REF]. Hypertension was much more prevalent in our study (60.0%) than in the literature (approximately 17%) [START_REF] Barrera | Prevalence of Diabetes and Hypertension and Their Associated Risks for Poor Outcomes in Covid-19 Patients[END_REF]. We did not find any other study exploring the prevalence of hypertriglyceridaemia. One previous study reported that approximately 20% of its study population had low HDL cholesterol [START_REF] Wang | Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study[END_REF].

Our results suggest that MetS was independently associated with COVID-19 deterioration. In two different studies, it was associated with mortality, invasive mechanical ventilation, and ICU transfer for COVID-19 [START_REF] Xie | Metabolic Syndrome and COVID-19 Mortality Among Adult Black Patients in New Orleans[END_REF][START_REF] Lohia | Metabolic syndrome and clinical outcomes in patients infected with COVID-19: Does age, sex, and race of the patient with metabolic syndrome matter?[END_REF]. In the study from Iran having included only patients admitted in ICU, the patients with MetS as compared to those without had a 3.3-fold increased risk of death [START_REF] Alamdari | The impact of metabolic syndrome on morbidity and mortality among intensive care unit admitted COVID-19 patients[END_REF].

In our study and as in another one [START_REF] Alamdari | The impact of metabolic syndrome on morbidity and mortality among intensive care unit admitted COVID-19 patients[END_REF], we found an association between a higher number of MetS components and more frequent COVID-19 deterioration. It would suggest that interassociation between all five MetS components, and their association with the disease, should be taken into account in order to identify patients most at risk of COVID-19 deterioration.

Elsewhere, plasma glucose levels and obesity at hospital admission have been associated with poor prognosis in COVID-19 patients with diabetes [START_REF] Cariou | Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study[END_REF]. Interestingly, another study highlighted that COVID-19 prognosis associated with type 1 diabetes was better than that for type 2 diabetes after adjustment for age [START_REF] Wargny | Type 1 Diabetes in People Hospitalized for COVID-19: New Insights From the CORONADO Study[END_REF], probably because individuals with type 1 diabetes rarely meet the criteria for MetS, especially android obesity.

COVID-19 deterioration is characterized by late and sudden respiratory deterioration (day 7 to 10) driven by a cytokine storm, resulting in lung infiltration and hyperactivation of innate immune cells [START_REF] Wang | Cytokine storm and leukocyte changes in mild versus severe SARS-CoV-2 infection: Review of 3939 COVID-19 patients in China and emerging pathogenesis and therapy concepts[END_REF]. Visceral fat, which characterizes MetS, is a well-known trigger of low grade systemic and chronic inflammation [START_REF] Mauvais-Jarvis | Aging, Male Sex, Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19[END_REF][START_REF] Mcardle | Mechanisms of obesity-induced inflammation and insulin resistance: insights into the emerging role of nutritional strategies[END_REF]. This permissive inflammatory environment increases the COVID-19 cytokine inflammatory storm and induces acute respiratory distress syndrome [START_REF] Mauvais-Jarvis | Aging, Male Sex, Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19[END_REF]. Furthermore, obesity is associated with leptin resistance [START_REF] Meckenstock | Modifications of immunity in obesity: The impact on the risk of infection[END_REF]. It impacts Tcells and natural killer cell differentiation, alters T CD8 and regulatory T-cells' number and function, inducing immunodepression [START_REF] Ibrahim | Subcutaneous and visceral adipose tissue: structural and functional differences[END_REF].

One limitation of our work is that the study population was not representative of all COVID-19 infected patients as we only investigated patients admitted for infection. Therefore, our results only apply to patients whose symptoms were too severe for them to be released homeeither because of respiratory symptoms or severe comorbidities -but not severe enough to warrant being transferred to ICU or directly intubated. We had considered that waist circumference would have been difficult to measure in patients in ICU. The study is small but we recruited 155 patients when 120 were necessary according to power calculation. The retrospective nature of our study exposed to risk for bias due to its and potential for missing or misclassified data. The strengths of our study include a multiethnic cohort likely to be translatable to different populations, despite single center lacking generalizability, and a pragmatic guidance-based approach. Finally, we defined MetS with its classical definition, considering especially waist circumference and triglycerides/ HDL-cholesterol measurements.

But we used HbA1c instead of fasting plasma glucose, as the latter may increase due to stress [START_REF] Li | From the insight of glucose metabolism disorder: Oxygen therapy and blood glucose monitoring are crucial for quarantined COVID-19 patients[END_REF]. However, we found similar results for MetS and MetSFPG.

Conclusion

Approximately 80% of patients admitted to Avicenne Hospital, Bobigny, France for COVID-19 had MetS. The patients with MetS might present with five times risk of deterioration as compared to patients without MetS. Identifying metabolic syndrome at admission could improve capacity of caregivers to predict COVID-19 deterioration.

Figure 1 :Additional Figure 1 :

 11 Figure 1: Prevalence of COVID-19 deterioration by metabolic syndrome and number of components of metabolic syndrome.

Table 1

 1 shows the general and medical characteristics of the study population. Additional Table1shows patients' main clinical, biological and radiological characteristics. A total of 56

	patients (36.1%) experienced COVID-19 deterioration, 25 patients (16.1%) needed non-
	invasive ventilation, and 22 patients (14.1%) were subsequently transferred to ICU. Mean
	hospitalization stay was 10.2±6.5 days. Nine patients (5.8%) died.

Table 1 :

 1 Population characteristics.

		Avalaible	Total	No COVID-19	COVID-19	p
				deterioration	deterioration
		data		n = 99	n = 56
	Age (years)	n=155	61.7±14.8	58.9±13.9	66.6±15.1	<0.01
	Male gender	n=155	86 (55.5)	50 (50.5)	36 (64.3)	0.10
	Ethnicity	n=149				0.13
	Caucasian		46 (30.9)	30 (30.9)	16 (30.8)
	Arabic		57 (38.3)	32 (33.0)	25 (48.1)
	Afro-Caribbean		26 (17.4)	18 (18.6)	8 (15.4)
	Asian		20 (13.4)	17 (17.5)	3 (5.8)
	Socioeconomic data	n=146				0.30
	Health insurance		142 (97.3)	90 (95.7)	52 (100.0)
	coverage
	No health insurance		4 (2.7)	4 (4.3)	0 (0.0)
	coverage
	Current smoker	n=148	3 (2.0)	3 (3.2)	0 (0.0)	0.55
	Medical history
	Cancer	n=155	16 (10.3)	9 (9.1)	7 (12.5)	0.50
	HIV infection	n=154	7 (4.5)	6 (6.1)	1 (1.8)	0.42
	Obstructive chronic	n=153	11 (7.2)	5 (5.1)	6 (10.9)	0.20
	bronchitis
	Charlson score	n=155				0.88
	0		66 (42.6)	44 (44.4)	22 (39.3)
	1-2		77 (49.7)	48 (48.5)	29 (51.8)
	3-4		9 (5.8)	5 (5.1)	4 (7.1)
	≥5		3 (1.9)	2 (2.0)	1 (1.8)
	Period of inclusion	n=155				0.09
	April-May 2020		94 (60.6)	65 (65.7)	29 (51.8)
	February-March		61 (39.4)	34 (34.3)	27 (48.2)
	2021
	Continuous variables are expressed as mean ± standard deviation. Categorical variables are
	expressed as number of patients (percentage). N: number of patients with available data.
	COVID-19 deterioration was defined as a need for nasal oxygen flow at or above 6 liters per
	minute.

COVID-19: Coronavirus Disease 2019; HIV: Human Immunodeficiency Virus.

Table 2 :

 2 Metabolic parameters description.Continuous variables are expressed as mean ± standard deviation. Categorical variables are expressed as number of patients (percentage). N: number of patients with available data COVID-19: Coronavirus Disease 2019. Deterioration defined as a need for nasal oxygen flow at or above 6 liters per minute. The five components of metabolic syndrome are (i) large waist circumference: waist circumference exceeding 88 cm for women and 102 cm for men, (ii) high HbA1c level (≥ 5.7%) or fasting hyperglycemia (fasting glycaemia ≥ 6.0 mmol/)L or pharmacologic treatment or known diabetes, (iii) high blood pressure: medical history of hypertension or antihypertensive medication introduced during hospitalization, (iv) high triglycerides level: triglycerides level ≥ 1.6 mmol/L or routine prescribed fibrate medication, and (v) low HDLcholesterol level: HDL-cholesterol level < 1 mmol/L in males or < 1.6 mmol/L in females or routine prescribed fibrate medication.

		N	Total	No COVID-	COVID-19	p
				19	Deterioration
				deterioration
				n=99	n=56
	Waist circumference-related
	parameters
	Android obesity	n=141	102 (72.3)	63 (67.7)	39 (81.3)	0.09
	Waist circumference in males (cm)	n=80	107±13	105±14	109±12	0.07
	Waist circumference in females	n=61	104±16	103±15	105±18	0.63
	(cm)
	Overweightness or obesity	n=152	118 (77.6)	76 (78.4)	42 (76.4)	0.78
	Obesity	n=152	70 (46.6)	48 (49.5)	22 (40.0)	0.26
	Body mass index (kg/m²)	n=152	29.8±6.1	30.2±6.2	29.2±6.1	0.42
	Glycaemia-related parameters
	Fasting hyperglycaemia	n=152	112 (73.7)	66 (67.3)	46 (85.2)	0.02
	Fasting glycaemia (mmol/l)	n=150	7.5±3.2	7.0±2.6	8.3±3.9	0.02
	Known diabetes	n=155	62 (40.0)	38 (38.4)	24 (42.9)	0.59
	HbA1c level ≥ 5.7%	n=151	135 (89.4)	83 (85.6)	52 (96.3)	0.04
	HbA1c (%)	n=149	7.2±2.2	7.1±2.2	7.5±2.3	0.11
	Blood pressure -related
	parameters
	High Blood pressure	n=155	93 (60.0)	55 (55.6)	38 (67.9)	0.13
	Known hypertension	n=155	91 (58.7)	55 (55.6)	36 (64.3)	0,29
	Lipid-related parameters
	Low HDL-cholesterol level	n=149	128 (85.9)	82 (85.4)	46 (86.8)	0.82
	HDL-cholesterol (mmol/l)	n=149	0.85±0.27	0.87±0.28	0.82±0.27	0.30
	High triglycerides level	n=151	66 (43.7)	34 (35.4)	32 (58.2)	<0.01
	Triglycerides (mmol/L)	n=151	1.75±0.83	1.72±0.93	1.81±0.6	0.04
	Other parameters
	Known obstructive sleep apnea	n=152	15 (9.9)	12 (12.2)	3 (5.6)	0.19
	Known non-alcoholic steatosis	n=151	11 (7.3)	9 (9.3)	2 (3.7)	0.33
	hepatitis

Table 3 :

 3 Multivariable analyses explaining COVID-19 deterioration COVID-19 deterioration was defined as a need for nasal oxygen flow at or above 6 liters per minute.

		Odds Ratios [95% confidence	p
		interval]
	Model 1 (n=149)
	Metabolic syndrome defined with high HbA1c level	5.3 [1.4; 20.2]	0.01
	Age (per 10-year interval)	1.7 [1.2; 2.4]	0.02
	Female gender	0.5 [0.2; 1.04]	0.06
	Period of inclusion (ref = April May 2020)	1.5 [0.7; 3.3]	0.27
	Charlson score (by 1 unit)	0.9 [0.6; 1.2]	0.5
	Afro-Caribbean-Asian	0.7 [0.3; 1.9]	0.4
	ethnicity (ref = Arabic)
	Caucasian ethnicity (ref =	0.5 [0.2; 1.3]	0.4
	Arabic)
	Model 2 (n=146)
	Metabolic syndrome defined	5.2 [1.5; 18.5]	<0.01
	with fasting hyperglycaemia
	Age (per 10-year interval)	1.8 [1.3; 2.6]	<0.001
	Female gender	0.4 [0.2; 0.9]	0.03
	Period of inclusion (ref=April	1.4 [0.6; 3.2]	0.4
	May 2020)
	Charlson score (by 1 unit)	0.8 [0.6; 1.2]	0.3
	Afro-Caribbean-Asian	0.7 [0.3; 1.9]	0.3
	ethnicity (ref = Arabic)
	Caucasian ethnicity (ref =	0.5 [0.2; 1.2]	0.3
	Arabic)

Acknowledgements

Contributors: EO, LA, OB and EC conceived and designed this study and had full access to all of the study data. They take responsibility for the integrity of the data and the accuracy of the data analyses. JM, CB and HB helped design the study and searched existing literature. EO, LA, IR and EC collected the data and EO, LA and EC drafted the paper. LA, EO, EC and CJ performed the analyses. All authors critically revised the manuscript for important intellectual content and gave final approval for the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved.

Acknowledgments

Our thanks to LVL Medical who partly funded this study. They were not involved in the collection, analyses or interpretation of data. Neither were they involved in writing of the manuscript or the decision to submit it for publication.

We also thank thank Rachida Mazouzi, Flory Mfutila Kaykay, Phuc Thu Trang Nguyen, Dr Fatma Kort and Dr Julien Caliez, for their help in collecting data. Furthermore, we thank SANOÏA-Real World Digital CRO for digital data collection and data management services, and Jude Sweeney (Milan, Italy) for the English editing and revision of the manuscript.

LVL Medical partly funded this study