Variation in albumin glycation rates in birds suggests resistance to relative hyperglycaemia rather than conformity to the pace of life syndrome hypothesis

  1. Adrián Moreno-Borrallo
  2. Sarahi Jaramillo-Ortiz
  3. Christine Schaeffer-Reiss
  4. Benoît Quintard
  5. Benjamin Rey
  6. Pierre Bize
  7. Vincent A Viblanc
  8. Thierry Boulinier
  9. Olivier Chastel
  10. Jorge S Gutiérrez
  11. Jose A Masero
  12. Fabrice Bertile
  13. François Criscuolo

  • Curated by eLife

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    eLife Assessment

    This important study uses extensive comparative analysis to examine the relationship between plasma glucose levels, albumin glycation levels, and diet and life history, within the framework of the "pace of life syndrome" hypothesis. The evidence that glucose is positively correlated with glycation levels and lifespan is convincing and, although there are some limitations related to data collection, they likely make the statistically significant findings more conservative. As the first extensive comparative analysis of glycation rates, life history, and glucose levels in birds, the study has the potential to be of interest to evolutionary ecologists and the aging research community more broadly.

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Abstract

The pace of life syndrome hypothesis (POLS) suggests that organisms’ life history, physiological and behavioural traits should co-evolve. In this framework, how glycaemia (i.e., blood glucose levels) and its reaction with proteins and other compounds (i.e. glycation) covary with life history traits remain relatively under-investigated, despite the well documented consequences of glucose and glycation on ageing, and therefore potentially on life history evolution. Birds are particularly relevant in this context given that they have the highest blood glucose levels within vertebrates and still higher mass-adjusted longevity when compared to organisms with similar physiology as mammals. We thus performed a comparative analysis on glucose and albumin glycation rates of 88 bird species from 22 orders, in relation to life history traits (body mass, clutch mass, maximum lifespan and developmental time) and diet. Glucose levels correlated positively with albumin glycation rates in a non-linear fashion, suggesting resistance to glycation in species with higher glucose levels. Plasma glucose levels decreased with increasing body mass but, contrary to what is predicted to the POLS hypothesis, glucose levels increased with maximum lifespan before reaching a plateau. Finally, terrestrial carnivores showed higher albumin glycation compared to omnivores despite not showing higher glucose, which we discuss may be related to additional factors as differential antioxidant levels or dietary composition in terms of fibres or polyunsaturated fatty acids. These results increase our knowledge about the diversity of glycaemia and glycation patterns across birds, pointing towards the existence of glycation resistance mechanisms within comparatively high glycaemic birds.

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  1. Version published to 10.7554/elife.103205.3 on eLife
  2. Version published to 10.7554/elife.103205 on eLife
  3. eLife

    eLife Assessment

    This important study uses extensive comparative analysis to examine the relationship between plasma glucose levels, albumin glycation levels, and diet and life history, within the framework of the "pace of life syndrome" hypothesis. The evidence that glucose is positively correlated with glycation levels and lifespan is convincing and, although there are some limitations related to data collection, they likely make the statistically significant findings more conservative. As the first extensive comparative analysis of glycation rates, life history, and glucose levels in birds, the study has the potential to be of interest to evolutionary ecologists and the aging research community more broadly.

  4. eLife

    Reviewer #2 (Public review):

    Summary:

    In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet and life-history traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contrast with findings of some previous studies (positive relationships between blood glucose and lifespan, or absent relationships between blood glucose and clutch mass or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that is based on data collected in a single study, with blood glucose and glycation measured using unified analytical methods (except for blood glucose data for 13 species collected from a database).

    Strengths:

    This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel and important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, except for 13 species, for which data were collected from a database of veterinary and animal care records of zoo animals (ZIMS). This novel data set itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a ZIMS database (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are substantiated. Overall, this is an important study representing a substantial contribution to the emerging field evolutionary physiology focused on ecology and evolution of blood/plasma glucose levels and resistance to glycation.

    Weaknesses:

    Unfortunately, the authors did not record handling time (i.e., time elapsed between capture and blood sampling), which may be an important source of noise because handling-stress-induced increase in blood glucose has previously been reported. Moreover, the authors themselves demonstrate that handling stress increases variance in blood glucose levels. Both effects (elevated mean and variance) are evident in Figure ESM1.2. However, this likely makes their significant findings regarding glucose levels and their associations with lifespan or glycation rate more conservative, as highlighted by the authors.

  5. eLife

    Author response:

    The following is the authors’ response to the previous reviews

    Public Reviews:

    Reviewer #2 (Public review):

    Summary

    In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contrast with findings of some previous studies (positive relationships between blood glucose and lifespan, or absent relationships between blood glucose and clutch mass or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that is based on data collected in a single study, with blood glucose and glycation measured using unified analytical methods (except for blood glucose data for 13 species collected from a database).

    Strengths

    This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel and important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, except for 13 species, for which data were collected from a database of veterinary and animal care records of zoo animals (ZIMS). This novel data set itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a ZIMS database (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are substantiated. Overall, this is an important study representing a substantial contribution to the emerging field evolutionary physiology focused on ecology and evolution of blood/plasma glucose levels and resistance to glycation.

    Weaknesses

    Unfortunately, the authors did not record handling time (i.e., time elapsed between capture and blood sampling), which may be an important source of noise because handling-stress-induced increase in blood glucose has previously been reported. Moreover, the authors themselves demonstrate that handling stress increases variance in blood glucose levels. Both effects (elevated mean and variance) are evident in Figure ESM1.2. However, this likely makes their significant findings regarding glucose levels and their associations with lifespan or glycation rate more conservative, as highlighted by the authors.

    Recommendations for the authors:

    Reviewer #2 (Recommendations for the authors):

    I understand that your main objective regarding glycation rate and lifespan, was to analyse the species resistance to glycation with respect to lifespan, while factoring out the species-specific variation in blood glucose level. However, I still believe that the absolute glycation level (i.e., not controlled for blood glucose level) may also be important for the evolution of lifespan. Given that blood glucose is positively related to both glycation and lifespan (although with a plateau in the latter case), lifespan could possibly be positively correlated with absolute glycation levels. If significant, that would be an interesting and counterintuitive finding, which would call for an explanation, thereby potentially stimulating further research. If not significant, it would show that long-lived species do not have higher glycation levels, despite having higher blood glucose levels, thereby strengthening your argument about higher resistance of longlived species to glycation. So, in my opinion, the inclusion of an additional model of glycation level on life-history traits, without controlling for blood glucose, is worth considering.

    We include now this model as supplementary material, indicating it in several parts of the text, including some of these issues we discussed here.

    Lines 230-231: Please, provide a citation for these GVIF thresholds

    We include it now.

    Figure 3: I think that showing both glucose and glycation rate on the linear scale, rather than log scale, would better illustrate your conclusion - the slowing rise of glycation rate with increasing glucose levels.

    That is a good point, although it may also be confusing for readers to see a graph that represents the data in a different way as the models. Maybe showing both graphs (as 3.A and 3.B) can solve it?

    Figure 4. I recommend stating in the caption that the whiskers do not represent interquartile ranges (a standard option in box plots) but credible intervals as mentioned in the current version of the public author response.

    Sorry about that, it was missed. Now it is included. Nevertheless, interquartile ranges from the posterior distributions can still be observed here represented with the boxes. Then the whiskers are the credible intervals.

  6. Version published to 10.7554/elife.103205.2 on eLife
  7. Version published to 10.1101/2024.07.02.600014v4 on bioRxiv
  8. eLife

    eLife Assessment

    This important study uses extensive comparative analysis to examine the relationship between plasma glucose levels, albumin glycation levels, and diet and life history, within the framework of the "pace of life syndrome" hypothesis. The evidence that glucose is positively correlated with glycation levels and lifespan is convincing and, although there are some limitations related to data collection, they likely make the statistically significant findings more conservative. As the first extensive comparative analysis of glycation rates, life history, and glucose levels in birds, the study has the potential to be of interest to evolutionary ecologists and the aging research community more broadly.

  9. eLife

    Reviewer #2 (Public review):

    Summary

    In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet and life-history traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contrast with findings of some previous studies (positive relationships between blood glucose and lifespan, or absent relationships between blood glucose and clutch mass or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that is based on data collected in a single study, with blood glucose and glycation measured using unified analytical methods (except for blood glucose data for 13 species collected from a database).

    Strengths

    This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel and important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, except for 13 species, for which data were collected from a database of veterinary and animal care records of zoo animals (ZIMS). This novel data set itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a ZIMS database (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are substantiated. Overall, this is an important study representing a substantial contribution to the emerging field evolutionary physiology focused on ecology and evolution of blood/plasma glucose levels and resistance to glycation.

    Weaknesses

    Unfortunately, the authors did not record handling time (i.e., time elapsed between capture and blood sampling), which may be an important source of noise because handling-stress-induced increase in blood glucose has previously been reported. Moreover, the authors themselves demonstrate that handling stress increases variance in blood glucose levels. Both effects (elevated mean and variance) are evident in Figure ESM1.2. However, this likely makes their significant findings regarding glucose levels and their associations with lifespan or glycation rate more conservative, as highlighted by the authors.

  10. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that

    (1) blood glucose levels predict albumin gylcation rates

    (2) larger species have lower blood glucose levels

    (3) lifespan positively correlates with blood glucose levels and

    (4) diet predicts albumin glycation rates.

    The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

    While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

    (1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

    (2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

    (3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

    (4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

    Reviewer #2 (Public review):

    Summary

    In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

    Strengths

    This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly wellsupported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

    Weaknesses

    My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a loglog model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

    Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

    We apologize for missing an answer to this part before. Indeed, glucose is always log transformed and this is explained in the text.

    The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

    Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

    It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

    Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the authors):

    (1) Line 84: "glycation scavengers" such as polyamines - can you specify what these polyamines do exactly?

    A clarification of what we mean with "glycation scavengers" is added.

    (2) Line 87-89: specify that the work of Wein et al. and this sentence is about birds.

    This is now clarified.

    (3) Line 95: "88 species" add "OF BIRDS". Also, I think it would be nice if you specified here that you are relying on primary data.

    This is now clarified (line 96).

    (4) Line 90-119: I find this paragraph very long and complex, with too many details on the methodology. For instance, I agree with listing your hypothesis, e.g. that with POL, but then what variables you use to measure the pace of life can go in the materials and methods section (so all lines between 112-119).

    This is explained here as a previous reviewer considered this presentation was indeed needed in the introduction.

    (5) Line 122-124: The first sentence should state that you collected blood samples from various sources, and list some examples: zoos? collaborators? designated wild captures? Stating the sample size before saying what you did to get them is a bit weird. Besides, you skipped a very important detail about how these samples were collected, when, where, and using what protocols. We know very well, that glucose levels can increase quickly with handling stress. Was this considered during the captures? Moreover, you state that you had 484 individuals, but how many samples in total? One per individual or more?

    We kindly ask the reviewer to read the multiple supplementary materials provided, in which the questions of source of the samples, potential stress effects and sample sizes for each model are addressed. All individuals contributed with one sample. More details about the general sources employed are given now in lines 125-127.

    (6) Line 135-36: numbers below 10 should be spelled out.

    Ok. Now that is changed.

    (7) Line 136: the first time I saw that you had both wild and captive samples. This should be among the first things to be described in the methods, as mentioned above.

    As stated above, details on this are included in the supplementary materials, but further clarifications have now been included in the main text (question 5).

    (8) Line 137-138: not clear. So you had 46 samples and 9 species. But what does the 3-3-3 sample mean? or for each species you chose 9 samples (no, cause that would be 81 samples in total)?

    This has now been clarified (lines 139-140).

    (9) Line 139-141: what methodological constraints? Too high glucose levels? Too little plasma?

    There were cases in which the device (glucometer) produced an unspecific error. This did not correspond to too high nor too low glucose levels, as these are differently signalled errors. Neither the manual nor the client service provided useful information to discern the cause. This may perhaps be related to the composition of the plasma of certain species, interfering with the measurement. Some clarifications have been added (lines 143-146).

    (10) Line 143: should be ZIMS.

    Corrected.

    (11) Line 120-148: you generally talk about individuals here, but I feel it would be more precise to use 'samples'.

    The use is totally interchangeable, as we never measured more than one sample for a given individual within this study. Besides, in some cases, saying “sample” could result less informative.

    (12) Line 150: missing the final number of measurements for glucose and glycation.

    Please, read the ESM6 (Table ESM6.1), where this information is given.

    (13) Line 154-155: so you took multiple samples from the same individual? It's the first time the text indicates so. Or do you mean technical replicates were not performed on the same samples?

    As previously indicated, each individual included only one sample. Replicates were done only for some individuals to validate the technique, as it would be unfeasible to perform replicates of all of them. This part of the text is referring to the fact that not all samples were analysed at the same time, as it takes a considerable amount of time, and the mass spectrometry devices are shared by other teams and project. Clarifications in this sense are now added (lines 160-163).

    (14) Line 171-172: "After realizing that diet classifications from AVONET were not always suitable for our purpose" - too informal. Try rephrasing, like "After determining that AVONET diet classifications did not align with our research needs...", but you still need to specify what was wrong with it and what was changed, based on what argument?

    The new formulation suggested by the reviewer has now been applied (lines 181-183). The details are given in the ESM6, as indicated in the text.

    (15) Line 174-176: You start a new paragraph, talking about missing values, but you do not specify what variable are you talking about. you talk about calculating means, but the last variable you mentioned was diet, so it's even more strange.

    We refer to life history traits. It has now been clarified in the text (line 185).

    (16) Line 177: what longevity records? Coming from where? How did you measure longevity? Maximum lifespan ever recorded? 80-90% longevity, life expectancy???

    We refer to maximum lifespan, as indicated in the introduction and in every other case throughout the manuscript. Clarifications have now been introduced (188-190).

    (17) Line 180-183: using ZIMS can be problematic, especially for maximum longevity. There are often individuals who had a wrong date of birth entered or individuals that were failed to be registered as dead. The extremes in this database are often way off. If you want to combine though, you can check the correlation of lifespans obtained from different sources for the overlapping species. If it's a strong correlation it can be ok, but intuitively this is problematic.

    The species for which we used ZIMS were those for which no other databases reported any values. We could try correlations for other species, but this issue is not necessarily restricted to ZIMS, as the primary origin of the data from other databases is often difficultly traceable. Also, ZIMS is potentially more updated that some of the other databases, mainly Amniotes database, from which we rely the most, as it includes the highest number of species in the most easily accessible format.

    (18) Line 181-186: in ZIMS you calculate the average of the competing records, otherwise you choose the max. Why use different preferences for the same data?

    This constitutes a misunderstanding, for which we include clarifications now (line 196). We were referring here to the fact that for maximum lifespan the maximum is always chosen, while for other variables an average is calculated.

    (19) Line 198: Burn-in and thinning interval is quite low compared to your number of iterations. How were model convergences checked?

    Please, check ESM1.

    (20) Line 201-203: What's the argument using these priors? Why not use noninformative ones? Do you have some a priori expectations? If so, it should be explained.

    Models have now been rerun with no expectations on the variance partitions so the priors are less informative, given the lack of firm expectations, and results are similar. Smaller nu values are also tried.

    (21) Line 217: "carried" OUT.

    Corrected (now in line 229).

    (22) Line 233-234: "species average model" - what is this? it was not described in the methods.

    Please, read the ESM6.

    (23) Line 232-246: (a) all this would be better described by a table or plot. You can highlight some interesting patterns, but describing it all in the text is not very useful I think, (b) statistically comparing orders represented by a single species is a bit odd.

    (a) Figure 1 shows this graphically, but this part was found to be quite short without descriptions by previous reviewers. (b) We recognise this limitation, but this part is not presented as one of the main results of the article, and just constitutes an attempt to illustrate very general patterns, in order to guide future research, as in most groups glycation has never been measured, so this still constitutes the best illustration of such patterns in the literature.

    (24) Line 281: the first time I saw "mass-adjusted maximum lifespan" - what is this, and how was it calculated? It should be described in the methods. But in any case, neither ratios, nor residuals should be used, but preferably the two variables should be entered side by side in the model.

    Please, see ESM6 for the explanations and justifications for all of this.

    (25) Line 281: there was also no mention of quadratic terms so far. How were polynomial effects tested/introduced in the models? Orthogonal polynomials? or x+ x^2?

    Please, read ESM6.

    (26) Table 1. What is 'Centred Log10Body mass', should be added in the methods.

    Please, read ESM6.

    (27) Table 1: what's the argument behind separating terrestrial and aquatic carnivores?

    This was mostly based on the a priori separation made in AVONET, but it is also used in a similar way by Szarka and Lendvai 2024 (comparative study on glucose in birds), where differences in glucose levels between piscivorous and carnivorous are reported. We had some reasons to think that certain differences in dietary nutrient composition, as discussed later, can make this difference relevant.

    (28) Table 1: The variable "Maximum lifespan" is discussed and plotted as 'massadjusted maximum lifespan' and 'residual maximum lifespan'. First, this is confusing, the same name should be used throughout and it should be defined in the methods section. Second, it seems that non-linear effects were tested by using x + x^2. This is problematic statistically, orthogonal polynomials should be used instead (check polyfunction in R). Also, how did you decide to test for non-linear effects in the case of lifespan but not the other continuous predictors? Should be described in the methods again.

    Please, read ESM6. Data exploration was performed prior to carry out these models. Orthogonal polynomials were considered to difficult the interpretation of the estimates and therefore the patterns predicted by the models, so raw polynomials were used. Clarifications have now been included in line 297.

    (29) Figure 2. From the figure label, now I see that relative lifespan is in fact residual. This is problematic, see Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of evolutionary biology, 22(7), 1367-1375. Using body mass and lifespan side by side is preferred. This would also avoid forcing more emphasis on body mass over lifespan meaning that you subjectively introduce body mass as a key predictor, but lifespan and body size are highly correlated, so by this, you remove a large portion of variance that might in fact be better explained by lifespan.

    Please, read ESM6 for justifications on the use of residuals.

    Reviewer #2 (Recommendations for the authors):

    (1) If the semi-logarithmic relationship (glycation ~ log10(glucose)) is to be used to support the hypothesis about higher glycation resistance in species with high blood glucose (lines 318-321 and 386-388), it should be tested whether it is significantly better than the model assuming a simple linear relationship (i.e., glycation ~ glucose). Alternatively, if the coefficient is to be used to determine whether glycation rate slows down or accelerates with increasing glucose levels, log-log model (log10(glycation) ~ log10(glucose)) assuming power function relationship (glycation = a * glucose^b) should be used (as is for example in the literature about relationships between metabolic rates and body size). Probably the best approach would be to compare all three models (linear, semi-logarithmic, and log-log) and test if one performs significantly better. If none of them, then the linear model should be selected as the most parsimonious.

    Different options (linear, both semi-logarithmic combinations and log-log) have now been tested, with similar results. All of the models confirm the pattern of a significant positive relationship between glucose and glycation. Moreover, when standardizing the variables (both glucose and glycation, either log transformed or not), the estimate of the slope is almost equal for all the models. It is also lower than one, which in the case of both the linear and log-log confirms the stated prediction. The log-log model, showing a much lower DIC than the linear version, is now shown as the final model.

    (2) ESM6, line 46: Please note that Kruskal-Wallis rank sum test in ESM1 shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values (not only higher variation). With this in mind, what is the argument here about increased variation being the main driver of stress-induced change in glucose levels based on? It seems that both the median values and variation differ between baseline and stress-induced levels, and this should be acknowledged here.

    As discussed in the public answers, Kruskal Wallis does not allow to determine differences in mean, but just says that the groups are “different” (implicitly, in their ranksums, which does not mean necessarily in mean), while the Levene test performed signals heteroskedasticity. This makes this feature of the data analytically more grounded. Of course, when looking at the data, a higher mean can be perceived, but nothing can be said about its statistical significance. Still, some subtle changes have been introduced in corresponding section of the ESM6.

    (3) Have you recorded the sampling times? If yes, why not control them in the models? It is at least highly advisable to include the sampling times in the data (ESM5).

    As indicated in ESM6 lines 42-43, we do not have sampling times for most of the individuals (only zebra finches and swifts), so this cannot be accounted for in the models.

    (4) If sampling times will remain uncontrolled statistically, I recommend mentioning this fact and its potential consequences (i.e., rather conservative results) in the Methods section of the main text, not only in ESM6.

    A brief description of this has now been included in the main text (lines 129-132), referencing the more detailed discussion on the supplementary materials. Some subtle changes have also been included in the “Possible effects of stress” section of the ESM6.

    (5) ESM6, lines 52-53: The lower repeatability in Tomasek et al.' study compared to your study is irrelevant to the argument about the conservative nature of your results (the difference in repeatability between both studies is most probably due to the broader taxonomic coverage of the current study). The important result in this context is that repeatability is lower when sampling time is not considered within Tomasek et al's data set (ESM1). Therefore, I suggest rewording "showing a lower species repeatability than that from our data" to "showing lower species repeatability when sampling time is not considered" to avoid confusion. Please also note that you refer here to species repeatability but, in ESM1, you calculate individual repeatability. Nevertheless, both individual and species repeatabilities are lower when not controlling for sampling time because the main driver, in that case, is an increased residual variance.

    We recognize the current confusion in the way the explanation is exposed, and have significantly changed the redaction of the section. However, we would like to indicate that ESM1 shows both species and individual repeatability (for Tomasek et al. 2022 data, for ours only species as we do not have repeated individual values). Changes are now made to make it more evident.

    (6) I recommend providing brief guidelines for the interpretation of VIFs to the readers, as well as a brief discussion of the obtained values and their potential importance.

    Thank you for the recommendation. We included a brief description in lines 230-231. Also in the results section (lines 389-393).

    (7) Line: 264: Please note that the variance explained by phylogeny obtained from the models with other (fixed) predictors does not relate to the traits (glucose or glycation) per se but to model residuals.

    We appreciate the indication, and this has been rephrased accordingly (lines 280-286).

    (8) Change the term "confidence intervals" to "credible intervals" throughout the paper, since confidence interval is a frequentist term and its interpretations are different from Bayesian credible interval.

    Thank you for the remark, this has now been changed.

    (9) Besides lifespan, have you also considered quadratic terms for body mass? The plot in Figure 2A suggests there might be a non-linear relationship too.

    A quadratic component of body mass has not shown any significant effect on glucose in an alternative model. Also, a model with linear instead of log glucose (as performed in other studies) did not perform better by comparing the DICs, despite both showing a significant relationship between glucose and body mass. Therefore, this model remains the best option considered as presented in the manuscript.

    (10) ESM6, lines 115-116: It is usually recommended that only factors with at least 6 or 8 levels are included as random effects because a lower number of levels is insufficient for a good estimation of variance.

    In a Bayesian approach this does not apply, as random and fixed factors are estimated similarly.

    (11) Typos and other minor issues:

    a) Line 66: Delete "related".

    b) Figure 2: "B" label is missing in the plot.

    c) Reference 9: Delete "Author".

    d) References 15 and 83 are duplicated. Keep only ref. 83, which has the correct citation details.

    e) ESM6, line 49: Change "GLLM" to "GLMM".

    Thank you for indicating this. Now it’s corrected.

  11. Version published to 10.1101/2024.07.02.600014v3 on bioRxiv
  12. eLife

    eLife Assessment

    This valuable study uses extensive comparative analysis to examine the relationship between plasma glucose levels, albumin glycation levels, and diet and life history, within the framework of the "pace of life syndrome" hypothesis. The evidence that glucose and glycation levels are broadly correlated is convincing. However, concerns about the consistency of the data quality across species and some aspects of data analysis make the key conclusion about higher glycation resistance in species with higher glucose levels currently incomplete. Still, as the first extensive comparative analysis of glycation rates, life history, and glucose levels in birds, the study has potential to be of interest to evolutionary ecologists and the aging research community more broadly.

  13. eLife

    Reviewer #1 (Public review):

    The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that
    (1) blood glucose levels predict albumin gylcation rates
    (2) larger species have lower blood glucose levels
    (3) lifespan positively correlates with blood glucose levels and
    (4) diet predicts albumin glycation rates.

    The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

    While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

    (1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

    (2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

    (3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

    (4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

  14. eLife

    Reviewer #2 (Public review):

    Summary

    In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and life-history traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

    Strengths

    This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly well-supported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

    Weaknesses

    My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a log-log model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

    Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

    The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

    Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

    It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

    Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

  15. eLife

    Author response:

    Reviewer #1:

    (1) This concern is addressed in the ESM6, and partly in the ESM1. Indeed, many of the concerns raised by the reviewer later are already addressed on the multiple supplementary materials provided, so we kindly ask the reviewer to read them before moving forward into the discussion.

    (2) This concern is reasonable, but its solution is not "extremely easy", as the reviewer states. The reviewer indicates the use of captive-based versus non-captive-based sources, remarking maximum lifespan, the main variable that is clearly expected to be systematically biased by the source of the data. Nevertheless, except for the ZIMS database, which includes only captive individuals, and some sources, as CNRS databases and EURING, which exclusively includes wild populations, the remaining databases, which are indeed where the vast majority of the data was collected from (i.e. Amniotes database, Birds of the World and AnAge) do not make any distinction. This means that they include just the maximum lifespan from the species as known by the authors of such databases' entries, regardless of provenance, which is also not usually made explicit by the database. Therefore, correcting for this would imply checking all the primary sources. Considering that these databases sometimes do not cite the primary source, but a secondary one, and that on several occasions such source is a specialized book that is not easily accessible, and still these referenced datasets may not indicate the source of the data, tracing all of this information becomes an arduous task, that would even render the usage of databases themselves useless. We will include some details about the concerns of database usage in the discussion to address this.

    Furthermore, it remains relevant to indicate that what we discuss later about the possible effects of captivity is about our usage of animals that come from both sources, not about the provenance of the literature-extracted data used (i.e. captive or wild maximum lifespan, for example), which is an independent matter. We can test for the first for next submission, but very difficultly could we test for the second (as the reviewer seems to be pointing to). In any case, as we do not have in any case the same species from both a captive and a wild source, it would be difficult to determine if the effect tested comes from captivity or from species-specific differences.

    (3) We will add data on the replicability of the glycation measurement in the next manuscript version. The CV for several individuals of different species measured repeated times is quite low (always below 2%).

    (4) The reviewer remarks reported here are already addressed on the supplementary material (ESM6), given the lack of space in the main manuscript. We therefore kindly ask the reviewer to read the supplementary material added to the submission. If the editors agree, all or a considerable part of this could be transferred to the main text for clarity, but this would severely extend the length of a text that the reviewer already considered very long.

    Reviewer #2:

    Thanks for spotting this issue with the coefficient, as it is actually a redaction mistake. It is a remnant of a previous version of the manuscript in which a log-log relation was performed instead. Previous reviewers raised concerns about the usage of log transformation for glycation, this variable being (theoretically) a proportion variable (to which we argue that it does not behave as such), which they considered not to be transformed with a logarithm. After this, we still finally took the decision of not to transform this variable. In this line, the transformations of variables were decided generally by preliminary data exploration. In this particular case, both approaches lead to the same conclusion of higher glycation resistance in the species with higher glucose. Nevertheless, we will consider exploring the comparison of different versions for the resubmission.

    About the issue related to handling time, this variable is not available, for the reasons already exposed in the answer to the other reviewer. Moreover, Kruskal-Wallis test, by its nature, does not determine differences in medians between groups per se, as the reviewer claims, but just differences in ranks-sums. It can be equivalently used for that purpose when the groups' distributions are similar, but not when they differ, as we see here with a difference in variance. What a significant outcome in a Kruskal-Wallis test tells us, thus, is just that the groups differ (in their ranks-sums), which here is plausibly caused by the higher variance in the stressed individuals. Even if we conclude that the average is higher in those groups, mere comparisons of averages for groups with very different variances render different interpretations than when homoscedasticity is met, particularly more so when the distribution of groups overlaps. For example, in a case like this, where the data is left censored (glucose levels cannot be lower than 0), most of this higher variance is related to many values in the stressed groups lying above all the baseline values. This, of course, would increase the average, but such a parameter would not mean the same as if the distributions did not overlap.

    Regarding the GVIFs, why the values are above 1.6 is not well known, but we do not consider this a major concern, as the values are never above 2.2, level usually considered more worrying. We will include a brief explanation of this in the results section. Also, we explicitly calculated life history variables adjusted for body mass, which should eliminate their otherwise strong correlation. There exist other biological and interpretational reasons justified in the ESM6 for using the residuals on the models, instead of the raw values, despite previously raised concerns.

    Given the asseveration by the reviewer that credible intervals are not to be used for the post hoc comparisons, as this is what the whiskers shown in Figure 4B represent, the affirmation of this graph suggesting any difference between groups remains doubtful. New comparisons have now been made with the function HPDinterval() applied to the differences between each diet category calculated from the posterior values of each group, confirming no significant differences exist.

    We do not understand the suggestion made in relation to the model shown in Table 2. Removing glucose from the model could have two results, as the reviewer indicates: 1. Maximum lifespan (ML) relates with glycation, potentially spuriously through the effect of glucose (in this case not included) on both; 2. ML does not relate to glycation, and therefore "high glycation levels do not preclude the evolution of long lifespans", which is what we are already showing with the current model, which also controls for glucose, in an attempt to determine if not just raw glycation values, but glycation resistance, relates to longevity. This is intended to asses if long-lived species may show mechanisms that avoid glycation, by showing levels lower than expected for a non-enzymatic reaction.

  16. Version published to 10.7554/elife.103205.1 on eLife
  17. Version published to 10.1101/2024.07.02.600014v2 on bioRxiv
  18. Version published to 10.1101/2024.07.02.600014v1 on bioRxiv