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

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 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.

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.