Glycated Albumin Levels in Roe Deer: a Marker of Body Condition modulated by Environment Quality

Glycated Albumin Levels in Roe Deer: a Marker of Body Condition modulated by Environment Quality | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 November 2025 V1 Latest version Share on Glycated Albumin Levels in Roe Deer: a Marker of Body Condition modulated by Environment Quality Authors : Cyrielle Duval , Benjamin Rey , Jean-François Lemaître , Emmanuelle Gilot-Fromont , Jean-Michel Gaillard , Maryline Pellerin , Sarahi Jaramillo Ortiz , Christine Schaeffer , Fabrice Bertile 0000-0001-5510-4868 , and Francois Criscuolo 0000-0001-8997-8184 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176217856.69234045/v1 216 views 164 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In humans, it is now widely accepted that the glycation reaction plays a major role in the senescence process, as well as in the development of associated pathologies. While the links between glycation and senescence have also been studied in laboratory animal models, they have hardly been studied in the wild. We therefore investigated the links between glycation and senescence in two populations of roe deer (Capreolus capreolus) facing markedly different environmental conditions, in which demographic and phenotypic senescence are well documented. Individuals of known age were captured in two years of differing quality. Individual data were collected (body mass, glycemia, sex), and we measured glycated albumin levels (GA) as well as six hematological parameters known to show senescence in this species. We found that GA did not correlate with age, blood glucose, or sex, but differed between populations and years of capture. In the population living in the low-quality environment, GA correlated negatively with the inflammatory status of individuals. Overall, it seems that for roe deer, GA reflects individual body condition rather than senescence, and are influenced by environmental quality. Introduction Senescence can be defined as the progressive deterioration of the structural integrity and function of cells and tissues with increasing age, ultimately leading to a decline in reproduction and survival (reproductive and actuarial senescence, respectively) (Monaghan et al. 2008). Patterns of senescence vary considerably in terms of age of onset and rate from one species to another, as well as among individuals within a given species (e.g. Jones et al. 2014; Cayuela et al. 2021). Such variability has been explained by sex (Maklakov and Lummaa 2013), as well as by various genetic (Roupakia et al. 2021, Saul et al. 2022), epigenetic (Crouch et al. 2022) or environmental (e.g., sociality level) factors (e.g. Berger et al. 2018). Recently, the number of studies aimed at identifying the physiological processes underlying senescence in the wild has risen sharply, involving, for example, mitochondrial dysfunction, oxidative stress and telomere attrition (van der Rijt et al. 2020; reviewed in López-Otín et al. 2023). Glycation, a spontaneous non-enzymatic reaction of free reducing sugars with free amino groups of proteins, DNA, and lipids, has been recognized as an aging reaction, i.e. one that alters the functionality of the proteins (Suji and Sivakami 2004), and is now also receiving particular attention in senescence studies. The binding of one or more reducing sugars to proteins, originally known as the Maillard reaction (Maillard 1912) and referred to here as protein glycation, leads to the formation of so-called Amadori compounds. Some Amadori compounds are essential biomarkers in human medicine, such as glycated haemoglobin HbA1c and glycated albumin (GA), which reflect blood glucose levels over the previous two weeks and one month, respectively. HbA1c and GA are thus used to monitor diabetes in humans (Koga 2014, Rabbani and Thornalley 2021a). Importantly, Amadori compounds can undergo further irreversible reactions to form stable advanced glycation end-products (AGEs) (Popova et al. 2010, Emel’yanov 2017), which tend to accumulate in cells and tissues over time (Akhter et al. 2021). When subjected to glycation, proteins undergo conformational changes (GhoshMoulick et al. 2007), leading to reduced functionality and subsequent alterations in various signalling and metabolic pathways. As a result, glycation products contribute to several aging-related diseases, such as Alzheimer, Parkinson, or diabetes complications, to name a few (Ansari and Rasheed 2009). There is also an increasing body of evidence supporting the role of glycation products in the process of cellular senescence (Fournet et al. 2018, Zgutka et al. 2023). AGEs and Amadori compounds have notably been shown to interfere with different physiological and cellular mechanisms, such as oxidative stress (Akhter et al. 2021), chronic inflammation (Davis et al. 2016), or telomere attrition (Deo et al. 2020). To date, most research on glycation has only been carried out in humans or in certain laboratory animal models. As recently reviewed (Duval et al. 2024), glycation and its link to senescence has hardly been studied in non-conventional animal models in natura . However, such studies could provide key mechanistic insights into our understanding of senescence patterns. Their relevance is further enhanced by the fact that the influence of glucose and glycation have already been linked to certain life-history traits (e.g., clutch size, Andersson and Gustafsson 1995 and growth rate, Ardia 2006), in several bird species. Interestingly, pentosidine (a cross-linking AGE) levels positively correlate with chronological age among several mammal species (Sell et al. 1991), but this is not the case for GA levels in the zebra finch ( Taeniopygia guttata ) (Brun et al. 2022) or for glycated hemoglobin levels in the collared flycatcher ( Ficedula albicolis ) (Andersson and Gustafsson 1995) and the domestic dog (Oikonomidis et al. 2022). Bearing in mind that glycation studies in mammals remain scarce and studies in the wild are expected to open the way to innovative treatments for diseases such as diabetes in humans (Duval et al. 2024), we investigated whether protein glycation (assessed through glycated albumin and glycated haemoglobin) could be related to senescence patterns in two populations of roe deer ( Capreolus capreolus ) living in the wild under markedly different ecological contexts, where both reproductive (Gaillard et al. 2003) and actuarial (Gaillard et al. 1993, Loison et al. 1999a) senescence have been documented. We notably tested whether glycation profiles were shaped by environmental (living conditions and sampling year) and individual (sex, body mass, glycemia, and age) factors. We hypothesized that, if glycation contributes to senescence in roe deer in the same way as it does in humans, then glycated protein levels should be higher in older individuals. Because glycation is a spontaneous reaction often described as being positively correlated with glycemia (Ketema and Kibret 2015, Yu et al. 2022), we tested this relationship in our animal model, with basically the same prediction. As reported in most mammalian species (Lemaître et al. 2020), males live shorter and senesce faster than females in roe deer (Gaillard et al. 1993, Loison et al. 1999a). If protein glycation underpins the senescence process in roe deer, we expect males to have higher levels of glycation products than females (for a same age), as has already been reported in human patients suffering from age-related diseases (Sharma et al. 2020) or in Sprague–Dawley and stroke-prone rats (Wang et al. 2006). Moreover, while no studies to date have looked at the impact of environmental factors on glycation levels, it is known that blood glucose levels can be influenced by ambient temperature, day length, or food availability, the latter being positively correlated to glycemia in stripped mice ( Rhabdomys pumilio ) (Schradin et al. 2015). Again, since Amadori compounds are expected to positively correlate with blood glucose levels (Copeland et al. 1987, Rabbani and Thornalley 2021b), we also expect glycation levels to vary among individuals facing different environmental conditions. We thus predicted that roe deer in the population living in a favourable ecological context (i.e. abundant food resources, low parasite pressure, mild weather conditions, or low population density), which display higher body mass (Douhard et al. 2017) and better health condition (telomere length, Wilbourn et al. 2017) at a given age, have developed some form of resistance towards glycation. We define such resistance as the mechanisms that either prevent proteins to be glycated or allow the rapid elimination of glycated proteins before they are converted into AGEs), and therefore display lower rates of glycation. In the two focal roe deer populations, year quality was assessed based on the annual survival probability of fawns during the same year, which provides a relevant surrogate of the overall quality of the year (Garratt et al. 2015), particularly in terms of food resources. Thus, juvenile survival is low in a poor-quality year, as opposed to high in a good-quality year. Therefore, as for habitat quality, we expect that better years (i.e. years with higher juvenile survival) should allow individuals to have an overall better body mass and health condition, which should result in lower glycation levels. In addition, we tested whether glycated albumin levels co-vary with several haematological parameters known to decline with increasing age and reflect health status and body condition (Jégo et al. 2014, Cheynel et al. 2017). Materials & Methods Study Populations We focused on two populations of roe deer (Capreolus capreolus) monitored in the wild that display markedly different environmental conditions. These populations are managed by the Office Français de la Biodiversité (OFB, formely Office National de la Chasse et de la Faune Sauvage). The “Territoire d’Etudes et d’Experimentation de Trois-Fontaines” is located in the North-East France (48°43N, 4°55E) and benefits from a continental climate, with rather cold winters and warm and rainy summers. Rich soils guarantee abundant and diverse food resources, resulting in a high-quality habitat for roe deer. On the other hand, the “Réserve Biologique de Chizé” is located in western France (46°050N, 0°250W) and has a temperate oceanic climate with Mediterranean influences, resulting in mild winters and frequent summer droughts (Pettorelli et al. 2006). This forest has poor-quality soils and limited food resources, and is thereby a poor-quality habitat for roe deer. Data Collection The Trois-Fontaines (TF) and Chizé (C) populations have been intensively monitored by capture-mark-recapture (CMR) since 1975 and 1977, respectively. Captures by drive-netting take place over 10–12 days each year, from mid-December to mid-March (Gaillard et al. 1993). All individuals caught are fitted with individually recognizable ear-tags and/or collars (either numbered, VHF, or GPS), given a unique identifier and closely monitored throughout their lives by subsequent winter captures and field observations. Once captured, the identity, sex, body mass (±50 g), and age (see below) are recorded. We then collected biological samples from individuals whose exact age was known, as they were captured either as newborns in the spring (see (Jullien et al. 1992) for more details) or at around 8 months of age during winter captures, when they most often have some milk incisors and always their milk premolars, Flerov 1952). We focused here on samples collected during two capture years (2020/2021 and 2021/2022) and for each study site (11 and 12 individuals of each population in each year), we selected an equal number of males and females of known age. We ended up with a total of 46 samples corresponding to 46 individuals in total. The sex- and site-specific age ranges were as follows: Trois-Fontaines females: 1-10 years old (mean ± SD = 5.1 ± 3.5); Trois-Fontaines males: 1-7 years old (mean ± SD = 5.1 ± 3.4); Chizé females: 1-15 years old (mean ± SD = 5.7 ± 4.1); Chizé males: 1-10 years old (mean ± SD = 5.7 ±3 .9). Blood samples (1 mL/kg to a maximum of 20mL) were collected from the jugular vein in 20 mL syringes attached with sterile hypodermic needles (gauge 19, 1.1 x 38mm). The blood was then distributed either into tubes containing a coagulation activator, which were used to prepare serum for immuno-hematology assays, or into heparin-lithium collection tubes (Vacutainer, BD Medical) to prepare plasma for subsequent assays of glucose concentration and protein glycation rates. Within 30 min of sampling, the tubes were centrifuged at 3,000 g for 10 min and the serum and plasma were transferred into cryogenic vials. Pellets from the heparin-lithium collection tubes containing red blood cells and leucocytes were then washed with an equivalent volume of 0.9% w/v NaCl solution. After a second centrifugation, we removed the buffy coat intermediate layer, mainly composed of leukocytes, and kept the washed red blood cells. All the samples were immediately frozen on site at −80°C in a portable freezer (Telstar SF 8025) until analyses. Serum proteins were separated and quantified thanks to an automatic gel electrophoresis procedure (HYDRASYS, Sebia, Evry, France), which enabled us to obtain albumin (ALB), alpha-1-, alpha-2-, beta- and gammaglobulin data (expressed in g/L). Creatinine (CREA, in μmol/L) was measured by enzymatic photometric determination at 540 nm (reagents: creatinine (enzymatic) 98 18 45, Thermo Electron SAS) (Jégo et al. 2014, Cheynel et al. 2017). Glycemia and Protein Glycation Rate Measurements Plasma glucose concentration was measured using a Contour Plus® 115 glucometer (Ascensia diabetes solutions, Basel, Switzerland), while protein glycation levels (for albumin in plasma and hemoglobin in red blood cells) were assessed by liquid chromatography-mass spectrometry (LC-MS) analysis, as previously described in Brun et al. (2022). Statistical Analyses All the analyses were performed using R version 4.3.1 (R Development Core Team 2015). We did not correct body mass by capture date neither in adults because adult body mass does not change seasonally (Andersen et al. 2000) nor in juveniles despite the fact that we know that there is an association population-specific (Douhard et al. 2017), since we had only 4 individuals of 1 year in our sample (2 males and 2 females, 3 from Trois-Fontaines and 1 from Chizé populations). We first aimed at identifying the best function describing the association between glycemia and chronological age. Following a standard approach in senescence studies (see e.g., Cambreling et al. 2023), we fitted four different models corresponding to (1) an absence of association with age (i.e., constant model), (2) a linear effect of age, (3) a quadratic effect of age and (4) a linear effect of age up to and/o from a threshold age using segmented regression using the package segmented (Muggeo 2008)). We used Akaike Information Criterion corrected for small sample size (AICc), and we selected the model with the lowest AICc (Arnold 2010). We did not detect any glycated haemoglobin in roe deer blood samples (see results section below), so we were only able to carry out analyses on glycated albumin. To assess changes in glycemia and GA levels in relation to population, sex, body mass, and year of capture, we used a linear model. Glycemia and GA were entered as the response variable, while the other variables were input as fixed effects. Because we looked for between-population differences, we only added the two-way interactions between population and the sex, body mass and year in both initial models. Age was not included in the set of independent variables, as the above-mentioned analysis revealed no age effect on glycemia or GA levels. Because we know that glycation levels positively correlate with glycemia in humans, we included the latter as an additional covariate in our GA model. Three-way interactions were omitted to avoid model over-fitting. Post-hoc tests were done using the pairwise-t-test function, using Bonferroni correction. To select the best model, we used an automatic selection procedure ( dredge function from the MuMIn package, Barton 2023). To avoid fitting over-parameterized models, we limited the number of variables to be retained in the selected models to 4, which ensured a ratio (sample size n / number of independent factors) greater than 10. We selected the models on the basis of the AICc following the procedure described above (Arnold 2010). The normality and homoscedasticity of residuals were verified through standard residual plot techniques along with a Shapiro-Wilk normality test, and the goodness-of-fit test was assessed through the conditional (total variance explained by the best model) and marginal (variance explained by fixed effects alone) R 2 . For the second part of this study aiming at assessing the relationships between glycation levels and haematological parameters previously reported to senesce in roe deer (Jégo et al. 2014, Cheynel et al. 2017), we first conducted a Principal Component Analysis (PCA) on those parameters to reduce the number of variables to include in our model (thus avoiding overfitting). Only 36 individuals were conserved for this analysis, those for which we had a complete dataset for all variables considered (17 in the TF population and 19 in the C population). We retained the two principal components (PCs) following the eigenvalues greater than 1, as an eigenvalue > 1 indicates that the PC accounts for more variance than accounted by one of the original variables in standardized data. We then included the two principal components (PCs) as continuous explanatory variables in a linear regression model, with glycated albumin as the response variable. Again, only the two-way interactions between population and the retained PCs were considered. The best model was selected in the same way as for the first part of the analysis described above. Results Blood Glucose and Protein Glycation Levels Do Not Correlate with Age Glycemia ranged from 65 mg.dL -1 to 363 mg.dL -1 across all samples, with a mean value ± SD of 182.5 ± 10.5 mg.dL -1 . For GA, we detected albumin forms only bearing a single glucose molecule. GA levels (expressed as a percentage of total albumin) in plasma ranged from 22.3 % to 28.6 %, with a mean ± SD of 25.7 ± 0.2 %. Interestingly, we did not detect glycated haemoglobin in any of our red blood samples. Contrary to our predictions, we did not detect an effect of age on glycemia, although the segmented regression was the model with the lowest AIC (ESM Table S1), suggesting an increase of glycemia until the age of 2.5 ± 0.8 years, followed by a plateau. For the age vs. glycated albumin relationship, we did not find any statistical significant link (i.e., the constant model was the selected model, ESM Table S2). Factors Influencing Glycemia and Glycated Albumin Levels When testing for the effect of population, sex, body mass and year of capture (and their interactions with population) on glycemia, only the year of capture effect was detected (Table 1, Figure 1.a.) while population and sex effects were marginal, despite being retained in the selected model (see ESM Table S3). In short, glycemia was higher in 2022 (211.0 ± 16.5 mg.dL -1 ) than in 2021 (153.9 ± 10.9 mg.dL -1 ), with individuals from Trois-Fontaines (i.e., the high-quality habitat) tending to have higher glycemia (201.4 ± 16.5 mg.dL -1 ) than those from Chizé (i.e., the poor-quality habitat, 165.9 ± 13.4 mg.dL -1 ). Body mass was not retained as an explanatory variable in our final model. Differences in GA levels among individuals were best explained by the interaction between population and year of capture (Table 2, Figure 2, ESM Table S4). In Chizé, individuals exhibited more GA in 2022 (26.2 ± 1.2 %) than in 2021 (24.6 ± 1.7 %, posthoc test, p = 0.047). In 2021, however, roe deer had higher GA levels in Trois-Fontaines than in Chizé (26.7 ± 1.3 % vs. 24.6 ± 1.7 %, respectively, posthoc test, p = 0.004), a pattern that held when considering both years, (26.2 ± 1.5 % and 25.4 ± 1.6 in Trois-Fontaines and Chizé, respectively) (Table 2). Body mass was found to be marginally and positively related to GA (see ESM Fig S1), while glycemia was not retained as an explanatory variable in our final model. Note that the most parsimonious model of the selection (without body mass, ESM Table S4) contained the interaction and all the effects included in our selected AICc model. Glycated Albumin Levels Reflect the Inflammatory States According to the Population The PCA carried out on the six haematological and inflammation markers (see ESM Figure S2) led us to retain the two first principal components PC1 and PC2, which explained 44.2% and 17.3% respectively, of the total variance of the dataset (Figure 3.a.). Beta-, gamma- and alpha-1- globulins and albumin concentrations contributed most to PC1 (29.5%, 19.4%, 18.6% and 17.6%, respectively), while creatinine and gamma-globulin were the main contributors to PC2 with 61.5% and 49.2%, respectively. All the variables mentioned were strongly positively correlated with their respective PCs (PC1: r= 0.88 for beta-globulin, r= 0.70 for alpha-1-globulin, r=0.72 for gamma-globulin; PC2: r = 0.79 for creatinine and r=0.48 for gamma-globulin), except for albumin which was strongly negatively related to PC1 (r=-0.68). PC1 therefore indicates the inflammatory state of individuals, primarily liver inflammation as indicated by the beta-gamma block (high values of PC1 correspond to potential liver inflammation, and general inflammation and infection) for a low albumin level, while PC2 is more likely a marker of renal state, with high values corresponding to a diminished efficiency of renal filtration. Our initial model included glycated albumin as a dependent variable, while PC1, PC2 and population were entered as continuous variables along with the two-way interactions of PC1/PC2 and population. After model selection, the interaction between the PC1 inflammatory axis and the population best explained the variation observed in GA levels (Table 3; ESM Table S5), showing a negative association between GA levels and PC1 in Chizé, and the opposite in Trois-Fontaines (Figure 3.b.). ESM figures S3 illustrate the population-specific relationship between PC1 and GA. Discussion In the present study, plasma glycated albumin levels varied in free-ranging roe deer, and we explored the possibility that GA could serve as a marker of senescence, potentially influenced by environmental conditions, individual health status, and sex. Glycated albumin levels were not associated with glycemia, age, or sex. However, they did differ between the two populations when considering the year of capture, and they also tended to increase with body mass. Therefore, only some of our predictions were supported, suggesting that glycated albumin levels should be considered as a marker of body condition and health rather than as a marker of senescence in roe deer. The absence of correlation between GA and glycemia might seem surprising given that glycation is an inevitable chemical reaction (Brownlee 1994). However, previous intra-specific studies in bats and birds reported similar results, showing that a positive relationship between glycemia and albumin glycation is not systematic (Moreno-Borrallo et al. in press, Brun et al. 2022). This absence of relationship between glycemia and GA levels could be attributed to the distinct effects glycemia and glycation may have on individual fitness (Montoya et al. 2018), which could result in different selection pressure on their respective regulation mechanisms, and thus end with no link. Another possible explanation is that the formation of compounds such as glycated albumin takes several days, and that albumin’s half-life is between 14 and 21 days (depending on the species). It therefore reflects blood glucose levels over a previous period of 2-3 weeks (Parrinello and Selvin 2014), which could explain the lack of correlation between the two measurements. One way to test this hypothesis would be to measure glycemia at time t and measure glycated albumin at time t+14 and t+21, and to determine whether a correlation emerges. We do know, however, that blood glucose levels are affected by short-term stressful events, such as the capture of individuals, which results in a rapid rise in post-capture glycemia (Whitehead 1966; Malisch et al. 2018; Lakušić et al. 2020). Such a short, one-off event would thus be detectable in the blood glucose measurement but would not be included in the GA measurement (that incorporates a longer time scale), and could again lead to a lack of correlation between the two measurements. It is noteworthy, however, that roe deer do not have higher average blood glucose (just a tendency was detected) but show higher GA levels in Trois-Fontaines than in Chizé, in particular in 2021. Interestingly, in 2022, individuals from both populations combined also had higher glycemia than in 2021. Again, this yearly variation is also true for GA, but mainly driven by individuals in the Chizé population, suggesting that, in a favourable environment, glycated-power of high glycemia may be buffered somehow. Those patterns and lack of co-variation calls for further study to determine the underlying physiological explanations. When looking at the co-variation between GA levels and age, the absence of correlation indicates that some old individuals exhibit high levels of GA, while others show lower levels (see ESM Figure S4). This shows that some individuals are able to live up to old age while having high levels of GA, thus exhibiting a better tolerance towards their pro-aging effects than other individuals. Our interpretation is that roe deer (at the species level) may be tolerant to the deleterious effects of glycated albumin in some ways. Furthermore, we know that in both populations, males show faster senescence than females. However, in the present study, males did not exhibit higher levels of glycated albumin, suggesting no link between the intensity of senescence and glycated albumin levels. These results indicate that glycated albumin cannot, so far, be considered as a marker of senescence in roe deer. This supposed tolerance to glycation in roe deer may be based on lower rates of transformation of Amadori’s products into AGEs and/or on the mitigation of the pro-senescent effect of AGEs (Nedić et al. 2013). Still, we cannot exclude a certain resistance of other proteins to glycation, since we were unable to detect glycated haemoglobin. This absence of detection might either reflect an extremely low level of glycated haemoglobin (below our lower limit of detection, knowing that in other species using the same LC-MS instrumentation, we were able to detect glycated haemoglobin rates as low as 1.4 ± 0.03%, Brun et al. 2022) or an effective complete absence of glycated haemoglobin in this species. As elevated levels of glycated haemoglobin reflect a malfunction in glycemic control over 3-6 months in humans (Roohk et al. 2018), this could suggest that roe deer are resistant to protein glycation in multiple ways (e.g., glycation of protein, removal of glycated proteins, or inhibited AGEs pathways). This hypothesis needs to be explored in parallel with comparable studies carried out on other long-lived animal species such as the roe deer, relying on high-sugar-diets and nevertheless exhibiting a comparable ability to resist to glucotoxicity (i.e., birds and bats, Polakof et al. 2011; Van de Weyer and Tahas 2024). Glycated albumin levels tended to increase with body mass. This is in contradiction with the few data published so far, which reported a negative relationship at the intra-specific level (Alpine swift, Tachymarptis melba , Moreno-Borrallo et al. 2024, see also Selvin et al. (2018) for a similar link with body mass index in humans). On the contrary, other studies failed to report any detectable relationship between GA levels and body mass (such as in Pteropus rodricensis , Duval 2024). This discrepancy is therefore not attributable to methodological bias of detection, but may rather rely on some biological or physiological peculiarity of roe deer. In our study case, heavier individuals had higher glycation levels, regardless of sex, population (i.e., environmental quality), glycemia, or age. As in most ungulates, larger and heavier male and female roe deer reach higher fitness, and body mass stands as an indicator of phenotypic quality (Toïgo et al. 2006). If roe deer have evolved some mechanisms to protect themselves from the deleterious effects of high levels of albumin glycation on fitness, and if glycated albumin simply covaries with body mass, then it is not surprising to find a negative association between the two variables. GA levels did vary between populations in 2021, with individuals from Trois-Fontaines (the high-quality habitat) exhibiting higher glycation levels than those from Chizé. Conversely, no detectable between-populations differences occurred in 2022. The fact that roe deer from Trois-Fontaines had higher glycation levels compared to those in Chizé and a better body condition suggests that this population might better tolerate the pro-aging effects of glycation. Moreover, when focusing solely on the Chizé population, we found that roe deer had higher GA levels in 2022 than in 2021. In Chizé, the summer survival of fawns was much higher in 2021 (0.550, 95% CI [0.089-0.939]) than in 2022 (0.231, 95% CI [0.116-0.407]) (Gaillard unpubl. data), highlighting that 2021 was overall a better year (notably in terms of food resources) than 2022. On the contrary, in Trois-Fontaines, 2022 was a better year than 2021 (with summer fawn survival of 0.653, 95% CI [0.409-0.837] vs. 0.428, 95% CI [0.215-0.671], respectively). No between-year difference in GA levels occurred in the Trois-Fontaines population. It thus seems that variation in low GA levels reflect a good year quality, especially in the population of Chizé. This might suggest that the population facing marked resource limitation, and consequently displaying a low phenotypic quality (evaluated through body mass (Douhard et al. 2017)) might be more easily impacted by sub-optimal environmental conditions, which appear to be reflected by higher glycated albumin levels. One could assume that a lower phenotypic quality might impact individual ability to regulate efficiently glycation levels, either through less efficient regulatory mechanisms and/or through a disabled glycation resistance (defined as the ability of a protein to escape glycation). In the Chizé population, when pooling both years, glycated albumin levels were negatively related to the inflammatory principal component, whereas no relationship was detected in Trois-Fontaines, where the GA levels remained stable with increasing inflammation. At first sight, this result might seem surprising and counter-intuitive, considering that in humans, increased GA levels are positively associated with increased inflammation (Ramasamy et al. 2005, Chuah et al. 2013, Davis et al. 2016, Roohk et al. 2018). One explanation could be that in roe deer, there is a decoupling between inflammation and glycation levels, potentially via protective mechanisms specifically targeting the pathways linking glycation products and inflammation. However, this result could also be interpreted as follows: roe deer in populations exhibiting different body mass and health might regulate both their GA levels and inflammatory response in different ways, which would result in a different correlation between the two parameters. Low levels of glycated albumin could thus reflect an unfavorable environment where individuals suffer from inflammation or infection. The most parsimonious explanation relies on the fact that proinflammatory status has been previously associated to an increase in protein turnover in humans, which is reflected by a decrease in circulating glycated albumin (Chagnac et al. 2003). This is observed in the Chizé population (see ESM Fig S3.d). Interestingly, glycated albumin has pro-inflammatory effects by stimulating pro-inflammatory cytokines such as TNF-α, IL-6 and IL-8 in humans (Koga et al. 2007). Hence, in roe deer, the negative relationship between glycated albumin levels and inflammation proxies in the Chizé population might reflect an adaptive response to mitigate inflammation. This would support the hypothesis that roe deer has evolved buffering mechanisms to tone down the pro-ageing signaling pathways that have been characterized in humans (Nedić et al. 2013). Moreover, the Chizé population was previously reported to have corticosterone-driven higher inflammatory status (Carbillet et al. 2023). Also, early-life conditions seem to shape immuno-senescence trajectories, through glucocorticoid mediation in roe deer populations (Lalande et al. 2024), which may also putatively explain the population-specific relationship we outlined in the present study. Therefore, how food resources, stress, glycation, and inflammation are intertwined over different life stages constitutes a clear next objective. Immuno-senescence (Cheynel et al. 2017) and senescence patterns in other traits such as creatinine and albumin levels (Jégo et al. 2014) or antler length (Cambreling et al. 2023) have been previously reported in the two studied populations. Due to our cross-sectional experimental design, which focuses on a relatively small sample size and over a large range of ages (1-15 years), we were unable to test the hypothesis that GA levels may be associated with longevity at the individual level, as well as with actuarial senescence. A crucial next step will be to conduct longitudinal studies of individuals in both populations, to determine the dynamics of GA over the life course. Still, this study already allowed us to demonstrate that 1) GA levels are likely to be a marker of phenotypic quality in the European roe deer, reflecting the environmental conditions individuals are facing with, and 2) that GA levels vary markedly between populations facing different environmental conditions (mostly in terms of habitat and year quality). Conflicts of interest : The authors declare that they have no conflict of interest. Ethics approval : The research presented in this article was done according to all institutional and/or national guidelines. The protocol of capture and blood sampling of roe deer is under the authority of the Office Français de la Biodiversité (OFB) and was approved by the Director of Food, Agriculture and Forest (Prefectoral order 2009-14 from Paris). All procedures were approved by the Ethical Committee of Lyon 1 University (project DR2014-09, June 5th, 2014). 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Keywords capreolus capreolus environmental quality glycation individual quality long-term data senescence Authors Affiliations Cyrielle Duval CNRS View all articles by this author Benjamin Rey Universite Claude Bernard Lyon 1 View all articles by this author Jean-François Lemaître CNRS View all articles by this author Emmanuelle Gilot-Fromont University Claude Bernard Lyon 1 View all articles by this author Jean-Michel Gaillard LBBE View all articles by this author Maryline Pellerin Office francais de la biodiversite View all articles by this author Sarahi Jaramillo Ortiz CNRS View all articles by this author Christine Schaeffer CNRS View all articles by this author Fabrice Bertile 0000-0001-5510-4868 View all articles by this author Francois Criscuolo 0000-0001-8997-8184 [email protected] CNRS View all articles by this author Metrics & Citations Metrics Article Usage 216 views 164 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Cyrielle Duval, Benjamin Rey, Jean-François Lemaître, et al. 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