Horizontal gene transfer (HGT) and gene loss result in rapid changes in the gene content of bacteria. While HGT aids bacteria to adapt to new environments, it also carries risks such as selfish genetic elements (SGEs). Here, we use modelling to study how HGT of slightly beneficial genes impacts growth rates of bacterial populations, and if bacterial collectives can evolve to take up DNA despite selfish elements. We find four classes of slightly beneficial genes: indispensable, enrichable, rescuable, and unrescuable genes. Rescuable genes — genes with small fitness benefits that are lost from the population without HGT — can be collectively retained by a community that engages in costly HGT. While this ‘gene-sharing’ cannot evolve in well-mixed cultures, it does evolve in a spatial population like a biofilm. Despite enabling infection by harmful SGEs, the uptake of foreign DNA is evolutionarily maintained by the hosts, explaining the coexistence of bacteria and SGEs. Most animals, including humans, inherit genes from their parents. However, bacteria and other microorganisms can also acquire genes from members of the same generation. This process, called horizontal gene transfer (HGT for short), allows bacteria to quickly adapt to new environments. For example, rather than waiting for rare mutations to arise, bacteria can pick up ‘tried and true’ genes from their neighbours which allow them to exploit new resources or become resistant to antibiotics.But gene sharing comes at a cost. For instance, taking up DNA is an energetically costly process and exposes bacteria to so-called selfish genes which replicate at the expense of other more useful genes in the genome. Given the costs and the threat of selfish genes, it remained unclear whether HGT is still beneficial in a stable environment where no new resources or antibiotics are present.Here, van Dijk et al. used mathematical modelling to examine how gene sharing affects the growth rate of bacterial colonies living in a stable environment. The experiments showed that bacteria are able to take up new sequences of DNA even in the presence of selfish genes. This allows communities of bacteria to retain genes that provide a small benefit that would otherwise be lost from the population, even when taking up DNA imposes a cost upon the individual. van Dijk et al. found that this collective behaviour cannot evolve in well-mixed bacterial populations, but readily emerged in more structured populations, such as biofilms.This work demonstrates how HGT, a key component of bacterial evolution, has allowed bacteria to coexist with harmful selfish genes. It also provides insights into how genes persist and spread through bacterial communities, which has implications for our understanding of antibiotic resistance. Horizontal Gene Transfer (HGT), the transmission of genetic material between unrelated individuals, is a major factor driving prokaryotic evolution . Recent copy genes from one individual to another. On the one hand, DNA uptake may be beneficial for the cells, as it allows adaptations to new environments , but also through recombination. For example, when bacteria take up DNA from their environment (i.e. transformation), genes inserted between two homologous regions may be integrated into the genome. The DNA that has been taken up may also carry mobile genetic elements (MGEs), which can integrate into the genome without a requirement for sequence similarity. Such processes have been coined ‘additive HGT’ , which ironments  or the rronments . On the ronments . Finallyronments . Hence, ronments . Given tA repair ?indispensable genes, that are never lost from the population, and for which HGT is therefore unnecessary and deleterious, (ii) enrichable genes, that are not lost from the population, but enriching the genes via HGT can nevertheless improve growth rates, (iii) rescuable genes, which are lost from the population without HGT, but can be rescued by HGT which improves population growth rates, (iv) unrescuable genes which are also lost from the population without HGT, but recovering them with HGT does not improve growth rates, and (v) selfish genetic elements, which confer a fitness penalty but can persist through HGT. For enrichable and rescuable genes, where HGT can increase population growth rates, we also investigate if HGT, that is the ability of cells to take up DNA, can evolve de novo. While the bacteria readily evolve to use HGT for enrichable genes, having sufficient donor cells to interact with, evolving HGT to ‘rescue’ rescuable genes faces a problem: HGT is needed for the gene to persist in the population, but sufficient donor cells are required to make HGT adaptive. This paradox is however resolved in a spatially structured population like a biofilm, as even a minority of donor cells can be locally abundant, giving rise to a localised ‘gene-sharing’ community that eventually overgrows the whole population. Finally, in this spatial eco-evolutionary context, HGT is evolutionarily maintained even when exploited by harmful genetic parasites, resulting in stable coexistence of bacteria and SGEs. Our model provides important insights and search images for how slightly beneficial genes may spread, or fail to spread, in an evolving microbial population.To address these questions, we here present and analyse a model of a bacterial population undergoing HGT of a single gene. We consider HGT through the uptake of genes from a shared pool of DNA, that is bacterial transformation. We assume that HGT is a costly process for the host cells, and that these costs are proportional to the rate of uptake. Such costs may reflect energetic costs for translocating DNA, growth impediments during the state of natural competence, or the various risks of incorporating foreign DNA into the genome. We show that this form of HGT has a positive impact on population growth rates by recovering slightly beneficial genes, which are hard to maintain in the population through selection alone. Based on whether or not the genes are lost from the population without HGT, and whether HGT can improve the population growth rate, we find that genes fall into one of five gene classes: (i) b, makes carriers grow faster than non-carriers , but carriers lose the beneficial gene at a fixed rate l. Non-carriers can recover genes by interacting with carriers through HGT. We study these dynamics with different models, first using simple ordinary differential equations , and cells that do not . The benefit of carrying the gene, ns ODEs,  and latens ODEs, . The equStarting with the simplest model depicted in To better understand the impact of HGT, we next study how HGT impacts the population growth rate  whether HGT of enrichable and rescuable genes is an evolutionarily stable strategy, and (ii) if bacteria can evolve this strategy de novo. To answer these questions, we consider two competing species: one that takes up DNA, and one that does not . In other words, invasion of arge see . This faonor see . As suchSo far, we have studied a well-mixed population of cells that undergoes all-against-all competition, and found that HGT is advantageous for slightly beneficial genes that (i) are not too beneficial, as these genes readily persist within the population without HGT, and (ii) are beneficial enough to compensate for the costly HGT. Next, we study the same dynamics of carrier and non-carrier cells in a spatially explicit, eco-evolutionary context. We do this by implementing an individual-based model (IBM), where bacterial cells reside on a grid, interactions are local, and events like HGT and gene loss are implemented as stochastic processes  is an evolutionarily stable strategy for a much broader range of To answer the question posed above, we allowed the HGT-rate ulations , consistdoes in fact emerge, therewith ‘rescuing’ the rescuable genes  enrichable genes which are not lost from the population without HGT, but moderate rates of HGT are adaptive, (iii) rescuable genes which are lost from the population without HGT, but can be rescued by HGT which improves population growth rates, and (iv) unrescuable genes, that are also lost from the population without HGT, but recovering them with HGT does not improve population growth rates, and (v) selfish genetic elements, genes that confer a fitness penalty, but can persist within the population with HGT. We further investigated if HGT of these respective gene classes is an evolutionarily stable strategy, and if the bacterial cells can evolve to engage in HGT de novo. We found that horizontal transfer of enrichable and rescuable genes is indeed a evolutionarily stable strategy, but can only evolve from scratch for enrichable genes. The evolution of HGT to ‘rescue’ a rescuable gene faces a problem under well-mixed conditions: HGT is required for the gene to persist, but sufficient carriers of the gene are necessary to evolve HGT. By modelling this process in a spatially structured population, we show that HGT can nevertheless evolve for these rescuable genes. As carriers of the gene can be locally abundant, emergent communities form that locally retain the gene via HGT, therewith slowly outgrowing other individuals. Finally, we show that once stable transferring communities have evolved, selfish genetic elements (SGEs) can stably coexist with the bacterial population and the beneficial genes. In spite of these genetic parasites exploiting the host’s ability to transfer, HGT is evolutionarily maintained, providing a doorway to the co-evolutionary process between bacteria and SGEs.We have studied the balance between the advantages and disadvantages of HGT by modelling a simple bacterial population undergoing the uptake of genes from a shared DNA pool. Our analysis shows that we can categorise slightly beneficial genes based on whether genes are lost from the population without HGT, and whether HGT of these genes can improve the population growth rate. This results in five distinct gene classes: (i) Here, we have used a simple model which ignores many of the complexities of natural bacterial populations, for example by assuming a constant environment and studying the impact of HGT one gene at a time. In reality, microbial ecosystems experience many changing selection pressures. Besides fluctuations in the available resources, traits like antimicrobial resistance, toxin production, and cooperative behaviour may confer large fitness benefits only under specific ecological circumstances . MoreoveWe have studied the impact of HGT by considering bacterial transformation as the mechanism by which genes transfer, that is by assuming (i) engaging in HGT is continuously costly and not only when it is successful, (ii) all carrier cells can act as a donor regardless of their ability to take up DNA themselves, and (iii) the ability for HGT is determined by the recipient cell. However, note that similar gene classes and comparable dynamics are observed when modifying these assumptions in our model see , and thaBesides investigating the impact of HGT for a range of different fitness effects, we have also shown how spatial structure is a key component for the emergence of HGT of rescuable genes. Both conjugation and transformation have indeed been observed to occur more frequently in biofilms than in well-mixed cultures , and plaIn nature, HGT can happen through a variety of mechanisms that each have their own potential advantages and disadvantages for the host cell . BacteriIn this work, we study the dynamics of bacteria undergoing HGT of slightly beneficial genes and Selfish Genetic Elements (SGEs). We do this by modelling the same processes with gradually increasing complexity, starting from simple Ordinary Differential Equations (ODEs), and then evaluating the same dynamics in an Invididual-based Model (IBM). A graphical representation of these models is found in the main text . The modi.e. mass-action). Our equations describing the density of carrier . Finally, we also extended the ODE model to study the impact on growth rates for cells that engage in HGT of both a beneficial gene and a Selfish Genetic Element (SGE), which can be found in the Supplementary Material.From the above model , we deriThe individual-based model (IBM) describes the same dynamics as the ODE models, but differs in some important aspects. Firstly, individuals are discrete entities that live on a 2D grid, and reproduce locally. This allows us to study the model with and without spatial pattern formation by modifying the rate at which cells mix. When mixing is disabled or very limited, a spatially structured population like that of a biofilm will form, while an increased amount of cellular mixing will approximate a well-mixed culture. Under well-mixed conditions, individuals will interact with random individuals in the population , while individuals will interact mostly with their conspecifics in case of the biofilm. We explicitly define a competition range  and a HGT range . These non-carrier cells, but genes fluxed in at a low rate , an R script that uses the deSolve R-package  in bacteria that explores the parameter space that would allow it to evolve. Interestingly, their model shows that even when costly, HGT is most like to evolve for genes with slight benefits to their hosts, which would be lost in the absence of HGT. HGT for these genes required a spatial context to evolve, but once these conditions were fulfilled, it evolved even in the presence of selfish genetic elements, which carry no benefit to their hosts.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Patricia Wittkopp as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Paul G Higgs (Reviewer #2).Thank you for submitting your article "Slightly beneficial genes are retained by collectives evolving Horizontal Gene Transfer despite selfish elements" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:This study uses a combination of two modelling approaches  to ask how HGT can evolve de novo, and what kind of genes will allow for selection and maintenance of HGT. The analysis reveals that depending on the benefit (or harm) they provide, genes can be categorised into indispensable, enrichable, rescuable, unrescuable or selfish. They analyse these different gene types and show that HGT is more likely to evolve and be maintained for "enrichable" genes that have a small benefit to their carriers or for both enrichable and rescuable genes if a spatial version of the model is considered.Essential revisions:All reviewers agreed that this is a very interesting paper that gives some useful ways to think about HGT (the gene categories) and important insights into its evolution. We also agreed that the modelling approaches used were interesting, informative and appropriate. The paper is well-written, in particular the figures, which are very visually appealing.A first key point is clarifying what mechanism of HGT the model is capturing: how is transfer occurring? What does the cost represent and how are genes acquired or lost? The paper contains mixed messages as to whether HGT occurs through plasmids or by transformation. According to the definition of "additive HGT", it should be through plasmids, and not by transformation, which requires recombination (Introduction paragraph two). But then other parts of the model (like acquiring genes from cells a certain distance away) suggest transformation. This choice will be important in determining the model structure, the rate of loss of the genetic element and its costs and benefits as detailed below.Regarding the cost, what does it represent, and why is the cost proportional to the rate of transfer (ch) rather than just a constant cost (c)? If the HGT mechanism is conjugation, there is a cost of replicating the plasmid inside the cell, but the cost is constant – it doesn't depend on how fast the genes are transferred, just on how many plasmids are in the cell. Alternatively, if fragments of DNA are taken up from the environment, maybe there is a small cost to expressing uptake genes, and this might be proportional to h, although one would expect the main fitness benefits and costs are associated with the genes/junk/SGEs that are acquired by HGT rather than simply from having the ability to acquire genes by HGT.- is the same as the loss rate from C+, which would be true if the gene is on a chromosome. But if that is the case, the gene that allows HGT should also be on the chromosome (contradicting with the additive HGT assumption) and it should also be lost sometimes, in which case a C+ could change to a C-, or an N+ to an N-. Another way to say this is "where does the C- come from?" If the beneficial gene were gained by HGT then it must have been gained by a + cell, then the C- was created by deletion of the gene that previously allowed HGT.Regarding gene loss, how it is modelled will also depend on the mechanism of HGT. If the gene is on a chromosome, then loss could be deletion of a gene during genome replication, or deleterious mutation that stops the function of the gene. On the other hand, if the beneficial gene is on a plasmid, loss of the beneficial gene is loss of the plasmid. This will clearly also affect the magnitude of the parameter value. In Equation 3, the loss rate from C+ represents a cell where the beneficial gene is on a plasmid that can be transferred by conjugation, whereas the C- represents a cell where the beneficial gene is already on a chromosome and cannot be transferred. In that case the loss rate from the chromosome by deletion should be different from the loss by losing the plasmid. Losing the plasmid means losing the benefit and the cost and the ability to transfer all at the same time. There is an interesting paper of Bergstrom that discusses the conditions for maintenance of a plasmid with a beneficial gene . It would be useful to discuss similarities and differences of the present model with the Bergstrom model. An important part of the Bergstrom model is the transfer of the beneficial gene to the chromosome. This seems to be analogous to the mutation of a C+ to a C- in this model. In Equation 3 the rate of gain of the beneficial gene is hN+(C- + C+). This means that C- cells can donate the gene. This would apply if the gene is acquired by transformation, rather than conjugation. How would this change in the conjugation case? The rate of gain would be hN+C+ only. The N+ cannot gain from the C-. Does that mean the plasmid cannot spread? I think the assumption that the cost of HGT is proportional to h is relevant here. If the cost were a constant c (not ch) then a plasmid with high enough h would invade whatever the cost to the cells.Another way to view the model is that the CFinally on loss, the current choice of parameter for the rate of loss is odd. The authors claim that their results hold for more realistic values (subsection “Parameters used”) but making the parameter-region where HGT is adaptive for the host cells narrower. Is the parameter region large enough to be biologically relevant?A second point that caused confusion is that the model assumes that each gene will either have a beneficial or a deleterious effect all the time. In reality, of course, MGEs lie on a parasitism-mutualism continuum relationship with the host bacterium because the “beneficial” gene will not be beneficial all the time . The same MGE transferring the beneficial gene may be an SGE depending on the bacterial host or the environment. The model thus considers a rather artificial case in which a single gene always has the same effect on its host. We see that it is a valid simplification to simulate each gene benefit individually, understand the conditions in which it may spread, and thereby infer that as long as genes with such benefits exist, then HGT should evolve. But this last logical step is not spelled out clearly enough in the paper.Whether or not the gene is beneficial may also change the mechanism of transfer, where genes that are beneficial in the long term are more likely to integrate on the chromosome and reducing the rate of loss possibly by orders of magnitude. This last point is crucial, because if the rate of loss is low enough then slightly beneficial genes could be maintained in the absence of HGT. In other words, the authors may want to consider a case where the rate of loss differs depending on the costs and benefits of the gene in a given context.A related point is how deleterious can SGEs become without disrupting HGT? This seems to be explored in Appendix 1—figure 5B and C. Is the result that SGEs would simply disappear if they are too deleterious? There seems to be a suggestion in the supplement that grid size may change this result. It would also be good to expand on this in the main text.eLife, we ask the authors to at least do the following:In sum, in order for the manuscript to be accepted in 1) Include a detailed discussion at the beginning of the paper about the assumptions of the model and which biological situation is represented. Are you simulating HGT by conjugation or transformation? Who decides whether transfer is possible – the donor, the receiver, or both?2) The authors should more explicitly explain where their parameter choices for costs, benefits, rates of loss, etc. and how they relate to the chosen mechanism of HGT. Also, why is it reasonable to assume that they are independent of each other?3) Revise the choices of cost, benefit and rates of loss according to the biological situation and taking into account the ideas discussed above. Will the conclusions hold and will they be biologically relevant ?4) More fully cite the relevant literature on HGT costs and how they arise 5) Flesh out what happens if SGEs are more deleterious. Essential revisions:All reviewers agreed that this is a very interesting paper that gives some useful ways to think about HGT (the gene categories) and important insights into its evolution. We also agreed that the modelling approaches used were interesting, informative and appropriate. The paper is well-written, in particular the figures, which are very visually appealing.A first key point is clarifying what mechanism of HGT the model is capturing: how is transfer occurring? What does the cost represent and how are genes acquired or lost? The paper contains mixed messages as to whether HGT occurs through plasmids or by transformation. According to the definition of "additive HGT", it should be through plasmids, and not by transformation, which requires recombination (Introduction paragraph two). But then other parts of the model (like acquiring genes from cells a certain distance away) suggest transformation. This choice will be important in determining the model structure, the rate of loss of the genetic element and its costs and benefits as detailed below.i.e. HGT is needed for the gene to persist, but the gene only persist with HGT) applies even more when C- cannot act as a donor, as as the case with conjugation.In our initial manuscript, we indeed are not clear on this. We modified the text to point out that the mechanism we study is most similar to HGT through bacterial transformation . However, we would also like to point out that many of the results in our manuscript may apply to HGT in the Abstract, generating useful search images for experimentalists and bioinformaticians. To show that our results could be insightful when different assumptions are considered, we added two supplementary paragraphs (and three figures) where discuss our model in light of different assumptions. We show that under various assumptions it is still fitting to talk about genes being rescuable (or not), indispensable (or not), and that the evolutionary “paradox” (i.e. through flanking homology / non-homologous recombination / transposons). This is relevant, as such additive incorporation of foreign DNA is essential for SGEs to be able to persist, which is why our model best applies to additive HGT (rather than replacing HGT).Next, we now elaborate on how additive gene transfer can also occur through the uptake of DNA  rather than just a constant cost (c)? If the HGT mechanism is conjugation, there is a cost of replicating the plasmid inside the cell, but the cost is constant – it doesn't depend on how fast the genes are transferred, just on how many plasmids are in the cell. Alternatively, if fragments of DNA are taken up from the environment, maybe there is a small cost to expressing uptake genes, and this might be proportional to h, although one would expect the main fitness benefits and costs are associated with the genes/junk/SGEs that are acquired by HGT rather than simply from having the ability to acquire genes by HGT.We now discuss that the costs are those that are applicable for transformation / natural competence . Most notably, (i) the expression and operation of DNA uptake machinery is costly, and (ii) the physiological state of competence is shown to stop cell division or paralyse core metabolic processes . Moreover, we argue that every HGT-event carries a risk, for example by chromosome disruptions, cytotoxicity, and the integration of SGEs. The latter SGEs are of course explicitly discussed in the remainder of the manuscript.We have also considered the model for constant costs, which is now included in a supplementary paragraph (and figure). These new supplementary results (Appendix 1 Part II) show that similar gene classes are observed for different assumptions for the costs of HGT. However, with fixed cost, the indispensable gene class is lost, as more and more HGT will always increase the frequency of carriers in the population. We consider these constant costs unrealistic however, as we expect that very high rates of HGT would eventually be a burden for the host cells (as explained above)- is the same as the loss rate from C+, which would be true if the gene is on a chromosome. But if that is the case, the gene that allows HGT should also be on the chromosome (contradicting with the additive HGT assumption) and it should also be lost sometimes, in which case a C+ could change to a C-, or an N+ to an N-. Another way to say this is "where does the C- come from?" If the beneficial gene were gained by HGT then it must have been gained by a + cell, then the C- was created by deletion of the gene that previously allowed HGT.Regarding gene loss, how it is modelled will also depend on the mechanism of HGT. If the gene is on a chromosome, then loss could be deletion of a gene during genome replication, or deleterious mutation that stops the function of the gene. On the other hand, if the beneficial gene is on a plasmid, loss of the beneficial gene is loss of the plasmid. This will clearly also affect the magnitude of the parameter value. In Equation 3, the loss rate from CAs mentioned above, it is our understanding that transformation can also lead to additive HGT.That is an interesting point. Indeed, given that the benefit of rescuing slightly beneficial genes is, by definition, also small, the gene(s) responsible for HGT may require HGT too. We have considered this secondary effect earlier in the project, but have not yet extensively analysed its implications. Although we agree with the reviewers that this would be an interesting extension of our analysis, we feel it would cloud the message of our current manuscript.+ represents a cell where the beneficial gene is on a plasmid that can be transferred by conjugation, whereas the C- represents a cell where the beneficial gene is already on a chromosome and cannot be transferred. In that case the loss rate from the chromosome by deletion should be different from the loss by losing the plasmid. Losing the plasmid means losing the benefit and the cost and the ability to transfer all at the same time. There is an interesting paper of Bergstrom that discusses the conditions for maintenance of a plasmid with a beneficial gene . It would be useful to discuss similarities and differences of the present model with the Bergstrom model. An important part of the Bergstrom model is the transfer of the beneficial gene to the chromosome. This seems to be analogous to the mutation of a C+ to a C- in this model. In Equation 3 the rate of gain of the beneficial gene is hN+(C- + C+). This means that C- cells can donate the gene. This would apply if the gene is acquired by transformation, rather than conjugation. How would this change in the conjugation case? The rate of gain would be hN+C+ only. The N+ cannot gain from the C-. Does that mean the plasmid cannot spread? I think the assumption that the cost of HGT is proportional to h is relevant here. If the cost were a constant c (not ch) then a plasmid with high enough h would invade whatever the cost to the cells.Another way to view the model is that the CThank you for pointing that out. We added a paragraph to the Discussion to elaborate on the comparison between conjugation (i.e. Bergstrom) and transformation (i.e. our work), and therein also speculate on promising directions for future modelling, to better contrast the two mechanisms.- can act as a donor does not change our initial analysis, which was done in the simpler model from Figure 1A. However, it does change some of the implications for evolution of HGT. The Allee effect (as discussed in the main text), now also exists for the enrichable genes when C- cannot act as a donor. An extra supplementary paragraph (and figure) now illustrates this. Interestingly, these new supplementary result suggest that the act of “rescuing” genes may be even more relevant with respect to conjugation.Changing whether or not CFinally on loss, the current choice of parameter for the rate of loss is odd. The authors claim that their results hold for more realistic values (subsection “Parameters used”) but making the parameter-region where HGT is adaptive for the host cells narrower. Is the parameter region large enough to be biologically relevant?b<l/c, meaning that our results may also apply to lower loss, depending on the costs. We do not know of any quantitative information to judge this issue on the balance between loss and costs accurately.The absolute value of the gene loss parameter indeed appears rather high, but bear in mind that the important point is the relationship between the costs and this loss rate. HGT is adaptive when A second point that caused confusion is that the model assumes that each gene will either have a beneficial or a deleterious effect all the time. In reality, of course, MGEs lie on a parasitism-mutualism continuum relationship with the host bacterium because the “beneficial” gene will not be beneficial all the time . The same MGE transferring the beneficial gene may be an SGE depending on the bacterial host or the environment. The model thus considers a rather artificial case in which a single gene always has the same effect on its host. We see that it is a valid simplification to simulate each gene benefit individually, understand the conditions in which it may spread, and thereby infer that as long as genes with such benefits exist, then HGT should evolve. But this last logical step is not spelled out clearly enough in the paper.Yes, that last logical step is important to point out. We elaborate on this in the (new) second paragraph of the Discussion. Here, we also discuss how much of our work was based on one of our earlier work. In this earlier eco-evolutionary model, we do study the co-evolution of beneficial genes and parasitic DNA, by allowing genes to evolve their own rate of transfer . Here, we found that rarely beneficial toxin genes would evolve very high gene mobility, while their corresponding resistance factors evolved minimal gene mobility.With the current model, our goal was to better understand how much of these dynamics could be explained simply by the benefit of these genes . We now refer back to this earlier work, to illustrate how our simple model shines light on dynamics that were first observed in a more complex eco-evolutionary context.Whether or not the gene is beneficial may also change the mechanism of transfer, where genes that are beneficial in the long term are more likely to integrate on the chromosome and reducing the rate of loss possibly by orders of magnitude. This last point is crucial, because if the rate of loss is low enough then slightly beneficial genes could be maintained in the absence of HGT. In other words, the authors may want to consider a case where the rate of loss differs depending on the costs and benefits of the gene in a given context.This comment is most relevant for conjugation. We now explicitly consider our results in light of transformation, and gene deletion then occurs through DNA deletion or substitution on the chromosome. While the dynamics of genes residing on plasmids vs the chromosome are indeed intriguing, it does not play a role in our current model.l is not particularly important, but the relationship between l and c is important. The reason why l and c are as they are in the IBM, is mainly to increase the speed of the relevant dynamics .This is not entirely accurate, as for any rate of gene loss > 0, there still is a class of genes that cannot be maintained without it, and can be maintained with HGT. As mentioned above, the absolute value of A related point is how deleterious can SGEs become without disrupting HGT? This seems to be explored in Appendix 1—figure 5B and C. Is the result that SGEs would simply disappear if they are too deleterious? There seems to be a suggestion in the supplement that grid size may change this result. It would also be good to expand on this in the main text.We have clarified in the text what happens with SGEs that are even more costly (subsection “HGT is evolutionarily maintained in the presence of harmful SGEs”). If HGT is not evolvable, nothing interesting happens when considering very costly SGEs, as the condition h > l – b still holds. Thus, no matter how costly, there is always a HGT-rate where the SGE can persist .i.e. when we allow the individuals in the IBM to evolve their rates DNA uptake), they will decrease their rates of HGT in the presence of costly SGEs. In the spatially structured populations, this can result in interesting oscillations in the HGT-rate, as described in the main text. However, as can be seen from Figure 6, the costly SGEs go through major bottlenecks. When even costlier SGEs are considered (see Appendix 1—figure 4), the selection pressures for decreasing HGT-rates eventually purges all SGEs from the system. For β=0.04 , the rate of HGT will continue to oscillate due to a tug-of-war between infected HGT+ strains and uninfected HGT- strain. As discussed in the main text, the HGT+ strain has the advantage of being able to maintain the rescuable gene, while the HGT- strain has the advantage of not being susceptible by SGEs. Due to the finite population size, the SGE is eventually lost, and the population will bounce back to higher rates of HGT . Even costlier SGEs (β = 0.05) do not persist at all, and quickly go extinct. Indeed, as mentioned, grid size does change these dynamics for β = 0.04, but we found that β = 0.05  could not persist even in larger populations (400x400 cells). Due to computational limitations, we have not tested what happens with even larger populations.When HGT is evolvable  Include a detailed discussion at the beginning of the paper about the assumptions of the model and which biological situation is represented. Are you simulating HGT by conjugation or transformation? Who decides whether transfer is possible – the donor, the receiver, or both?Since one of our main research questions is “When is taking up a gene beneficial for the bacteria?”, the model best represents bacterial transformation, where cells take up DNA from their environment. We now make it explicit that we consider transformation , where the receiver decides whether transfer is possible. We also address the costs that this mechanism may entail (Introduction paragraph two and three).2) The authors should more explicitly explain where their parameter choices for costs, benefits, rates of loss, etc. and how they relate to the chosen mechanism of HGT. Also, why is it reasonable to assume that they are independent of each other?This is much clearer in the revised manuscript. For transformation, the independence of these parameters is a more reasonable assumption than for conjugation. Nevertheless, one may argue that higher rates of HGT may cause more gene loss due to illegitimate recombination / insertion into coding regions. Either way, as we do not know the precise values of these parameters and how they may correlate, the model allows us to consider the impact of 1 variable at a time.3) Revise the choices of cost, benefit and rates of loss according to the biological situation and taking into account the ideas discussed above. Will the conclusions hold and will they be biologically relevant ?if the parameter range for rescuable/enrichable genes is more narrow in reality, then the ability to evolve HGT will depend on the efficiency of selection (population size and mutation rates), and as mentioned by the reviewers, on the rate at which the gene for HGT is itself lost. We (and others) should take that final point into account for future modelling.The statement that the parameter range is more narrow for lower loss  may have been misleading, and has been changed to avoid this confusion. Whatever the absolute loss rate, it is the relationship between loss and the costs that enable/hinders the evolution of HGT. This is now explained in the Materials and methods (section “Parameters used”). That said, 4) More fully cite the relevant literature on HGT costs and how they arise We now cited both these papers. As San Millan and MacLean references specifically the costs of plasmids, this is cited when comparing our results with what is known about conjugation and plasmids  in the Discussion.5) Flesh out what happens if SGEs are more deleterious.We added descriptions of what happens with very costly SGEs in the main text. (subsection “HGT is evolutionarily maintained in the presence of harmful SGEs”)


Is hearing impairment associated with cardiovascular disease risk and cognitive function among Hispanic or Latino participants?In this cohort study of 9623 Hispanic/Latino adults, hearing impairment was associated with poorer cognitive performance on all tasks, and cardiovascular disease risk did not attenuate these relationships. Rather, hearing impairment modified the associations between cardiovascular disease risk and learning and memory; only among individuals with hearing impairment, being identified as having excessively high glucose was associated with poorer learning and memory relative to participants considered healthy individuals.Hearing impairment may exacerbate the associations between high glucose and poorer cognition, particularly for learning and memory among Hispanic or Latino persons. This cohort study examines associations between hearing impairment, cardiovascular disease risk, and cognitive function in the Hispanic/Latino population in 4 US cities. Both cardiovascular disease risk and hearing impairment are associated with cognitive dysfunction. However, the combined influence of the 2 risk factors on cognition is not well characterized.To examine associations between hearing impairment, cardiovascular disease risk, and cognitive function.This population-based, prospective cohort, multisite cross-sectional analysis of baseline data collected between 2008 and 2011 as part of the Hispanic Community Health Study/Study of Latinos included 9623 Hispanic or Latino adults aged 45 to 74 years in New York, Chicago, Miami, and San Diego.Hearing impairment of at least mild severity was defined as the pure tone average of 500, 1000, 2000, and 4000 Hz greater than 25 dB hearing level (dB HL) in the better ear. Our measure of cardiovascular disease risk was a latent class variable derived from body mass index, ankle-brachial index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, fasting blood glucose, and the Framingham Cardiovascular Risk score.Results on Brief-Spanish English Verbal Learning Test (episodic learning and memory), and Word Fluency , and Digit Symbol Subtest (processing speed/executive functioning), and a cognitive composite of the mentioned tests .F3 = 3.70 and F3 = 2.92, respectively).Participants (N = 9180) were 54.4% female and age 56.5 years on average. Hearing impairment was associated with poorer performance on all cognitive measures . Cardiovascular grouping  did not attenuate the associations between hearing impairment and cognition . However, cardiovascular grouping interacted with hearing impairment such that hyperglycemia in the context of hearing impairment exacerbated poor performance on learning and memory tasks (The findings of this cohort study suggest that hearing impairment increases the likelihood that individuals with excessively high glucose perform poorly on learning and memory tasks. Further research is needed to specify the mechanisms by which cardiovascular disease risk and hearing impairment are collectively associated with cognition. Most attention has been placed on managing cardiovascular disease risk (CVDR) factors.4 The CVDR factors and mechanisms, including blood pressure dysregulation, arteriosclerosis, inflammation, and diabetes have been implicated across the spectrum of cognitive dysfunction from mild cognitive impairment to ADRD.6 Some data suggest that CVDR may have a stronger role in cognitive dysfunction and ADRD risk among Hispanic or Latino participants relative to non-Hispanic or Latino participants.9Forty-seven percent of US adults aged 65 years and older report some degree of memory impairment,14 incident cognitive impairment, and incident ADRD,15 but results are inconsistent.16 Although hearing impairment has a poor positive predictive value for cognitive impairment or dementia over a 10-year period,12 it has been suggested that treatment of hearing loss may reduce the risk of dementia.17 Despite high prevalence of both hearing impairment19 and ADRD20 among Hispanic or Latino participants, few studies have examined the connections between hearing impairment and cognition among this population.21 One exception is a recent study by Golub and colleagues22 which observed links between hearing impairment and cognition among a diverse group of middle-aged and older Hispanic or Latino participants.More recently, hearing impairment has been associated with reduced cognitive function,23 As with the central nervous system, the auditory system is vulnerable to vascular pathophysiology.24 Epidemiologic evidence demonstrates that both hearing impairment and cognitive dysfunction are associated with CVDR factors .26 Several studies among older adults support an association between hearing loss and poorer cognition, independent of smoking, diabetes, and hypertension,27 but studies have not fully assessed vascular contributions. Importantly, Golub and colleagues22 did not find that controlling for cardiovascular disease attenuated the associations between hearing impairment and cognition among aging Hispanic or Latino participants. However, it is unclear as to whether having 2 risk factors for ADRD  exacerbates poor cognitive performance.Associations between hearing impairment and age-related cognitive decline may reflect a common cardiovascular pathway affecting nervous systems.22 of the associations between hearing impairment and cognition to include vascular contributions in the same cohort of diverse Hispanic or Latino participants. We hypothesized that (1) an inverse association between hearing impairment and cognitive function would be attenuated by controlling for CVDR and (2) the strength of the association between CVDR and cognitive function would vary by hearing impairment.The aim of this analysis was to expand on the study by Golub and colleagues29 In this study, we examined the oversampled middle-aged and older adults (45-74 years) who underwent neurocognitive testing from 2008 to 2011. Of these participants (n = 9623), we excluded 290 participants owing to missing data on model covariates (see sociodemographic covariates section). In addition, 153 participants did not have data to assess hearing impairment. Therefore, the primary analytical sample consisted of 9180 participants. Detailed presentation of missing data patterns and differences in sociodemographic and health characteristics are presented in eTables 1 and 2 in the The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multisite , probability sampled, prospective cohort study of self-identified Hispanic or Latino participants aged 18 to 74 years. Study design, rationale, and implementation have been previously reported.31 The B-SEVLT is an episodic verbal learning and memory test with 2 scores for: (1) learning , and following an interference trial (2) memory . The WF is a phonemic verbal fluency test . The DSS is a mental processing speed and executive functioning examination . These HCHS/SOL cognitive tests, scoring procedures, and application to the HCHS/SOL data have been previously reported.33 All measures were z-score transformed  to facilitate score comparisons across tests using a common metric. A global cognitive score was derived by averaging the z-scores across the 4 domain-specific tests, as described herein.Tests included the Brief-Spanish English Verbal Learning Test (B-SEVLT), Word Fluency (WF), and Digit Symbol Substitution (DSS) test.34Audiometric pure-tone air conduction hearing thresholds in decibels (hearing level) [dB HL] were obtained by trained, certified technicians for each ear at 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz using calibrated GSI-61 clinical audiometers with TDH-50 headphones in sound-attenuating booths. A modified Hughson-Westlake procedure was used in accordance with American Speech Language Association guidelines.18For each individual and ear, we averaged air-conduction pure-tone thresholds measured at 500, 1000, 2000, and 4000 Hz to produce a pure-tone average (PTA), as a measure of speech-frequency hearing sensitivity. We identified participants with bilateral hearing impairment of at least mild severity using a greater than 25 dB HL PTA threshold in the better ear.36 Participant BMI was calculated as body weight in kilograms divided by height in meters squared.37 Participant ABI was based on the average ankle to arm systolic pressures as measured from the left and right sides. For each of the left and right sides, the ABI was calculated as the maximum systolic blood pressure in the posterior tibial artery or the dorsalis pedis artery in the same leg, divided by the maximum systolic blood pressures in the left and right brachial arteries. The overall composite ABI was then calculated for each participant as the minimum of the left- and right-side ABI.38 Fasting plasma lipid panels included total cholesterol (mg/dL), LDL, triglycerides, and HDL cholesterol, none of which required use of preparative ultracentrifuge. Participant LDL (mg/dL) was calculated using the Friedewald equation where LDL cholesterol = total cholesterol – HDL cholesterol – (triglycerides/5).40 Participant HDL (mg/dL) was measured with a direct magnesium/dextran sulfate method. Serum triglycerides (mg/dL) were measured via a Roche Modular P chemistry analyzer using a glycerol blanking enzymatic method.41 To capture glycemia, FBG (mg/dL) was measured using a hexokinase enzymatic method . Finally, 10-year Framingham Risk Score was estimated using sex-specific published criteria.42We modeled CVDR using latent profile analysis (LPA) techniques, which allow for identification of unobservable, data-derived subgroups. Latent profile analyses have been used extensively to model disease profiles , patient symptom experiences , and patient-related outcomes . The CVDR groups were estimated using 8 measures : body ma43 and field center .We included the following list of confounding variables associated with cognitive function: age in years, sex , education , Hispanic or Latino background , annual household income measured in 4 brackets , marital status , Center for Epidemiologic Studies Depression Scale Revised (CES-D-10),Our analyses were conducted in 3 steps. First, we fit LPA models following standard procedures and sequentially assessed fitted class solutions  age, sex, education and center-adjusted, and (2) CVDR group-adjusted, and (3) full-covariates adjusted models and calculated and plotted average marginal estimates of cognitive performance by hearing impairment status using post hoc estimation techniques. Prior to assessing modifications in the associations between CVDR grouping and cognition by hearing impairment status, we fit survey logistic regression models to examine associations between CVDR groups and hearing impairment. Finally, we refit the models to test whether hearing impairment modified the association of CVDR classification and neurocognitive outcome by including an interaction term between hearing impairment and CVDR classification.Three sensitivity analyses were conducted. Because the use of hearing aids may be a confounder in the relationship between hearing impairment and cognition, we conducted a sensitivity analysis repeating the described survey regression steps restricting the analytics sample to participants aged 45 to 74 years who denied hearing aid use. Second, we tested modifications in the associations between CVDR grouping and cognition by a continuous measurement of pure tone average in the better ear . Third, we also modeled a 3-class solution derived from the LPA to examine whether our findings were robust to a classification that included 3 groups only (rather than the adopted 4-class solution).46 We used MPLUS statistical software  to generate the LPA models, Stata statistical software  to generate descriptive statistics and survey-adjusted regression models and visualization.All analyses accounted for the complex design and probability weights of the HCHS/SOL.The CVDR profiles for each class membership  were conIndividuals meeting criteria for hearing impairment were primarily male, had less than 12 years of education, and a yearly household income less than $20 000. Individuals with hearing impairment were older and had a worse cardiovascular profile on all the considered risk factors, with the exceptions of BMI and ABI .Individuals classified as high CVDR  and hyperglycemia  had 48% and 64% higher odds ratios of hearing impairment compared with those in the healthy group, after adjusting for age, sex, education, and field center. Typical and healthy groups were not statistically distinct  and consistently lower cognitive function on all considered cognitive domains (z-score units), including verbal learning and memory , verbal fluency , and processing speed/executive functioning  (Hearing impairment (>25 dB) was associated with lower (z-score units) global cognition  . With th, 0.03]) .In age, sex, education, and field-center adjusted models, the high-CVDR and hyperglycemia groups were associated with lower global cognitive function . High CVDR classification was linked to consistently lower cognitive function on all considered domains; whereas hyperglycemia classification was associated with lower performance on verbal fluency and processing speed/executive functioning, only. These associations remained consistent after full covariate adjustment.Adjusting for all covariates, we found evidence to support hearing impairment modifications that were specific to verbal learning  and memory   cohort, suggesting that mild hearing impairment (>25 dB) is associated with cognition prior to older adulthood. However, it is unclear if these early patterns are specific to Hispanic or Latino participants. Among a predominantly White sample,52 mild hearing impairment was only associated with faster rate of decline, not baseline cognitive performance, across a range of tasks. Others have detected poorer cognitive performance with moderate-to-severe, but not mild, hearing impairment.53 Alternatively, our sample was less educated than other samples,53 and lower education combined with mild hearing impairment has been associated with faster cognitive decline.52 Supporting this notion, mild hearing impairment was associated with lower cognition among Japanese individuals with lower education (>80% with ≤12 years of education).54 More recently, subclinical hearing impairment (≤15 dB) was associated with poorer cognition in both the HCHS/SOL and the somewhat higher educated National Health and Nutrition Examination Study cohorts,21 suggesting that this is not specific to Hispanic or Latino participants nor to those with low education.Consistent with existing literature,18 hyperglycemia classification was associated with increased hearing impairment. We also identified more robust associations between presence of multiple CVDR factors  and hearing impairment relative to previous work. Our restricted age range  may have increased the proportion of individuals with CVDR burden, which limited selective attrition due to earlier deaths, and thus increased our sensitivity to detect associations.Findings of this study extend the current literature on risk factors for cognitive impairment by showing the nuanced interplay between hearing impairment and CVDR. First, in line with previous HCHS/SOL findings,22 Given that CVDR did not attenuate the relationships between hearing impairment and cognition, our results do not support a cardiovascular common cause of hearing impairment and cognitive impairment.48 However, longitudinal data are required to confirm, and our results do not rule out the possibility of an altogether different factor that effects CVDR, hearing impairment, and cognition .55Second, although hearing impairment and CVDR were associated with one another, both factors appeared to have independent associations with cognition. Previous work has shown similar relationships between hearing impairment and cognition when controlling for CVDR, but did not examine the plausible role of CVDR.56 However, the high-CVDR group was independently associated with poorer cognition across all domains, and such relationships were not contingent on or exacerbated by hearing impairment. Both hearing impairment58 and high CVDR59 have been associated with changes in brain structure, which may compromise cognitive functioning. Neuroimaging may aid in determining if the presence of multiple CVDRs are sufficient to affect brain structure and cognition, whereas hyperglycemia may be insufficient unless paired with another risk factor , thus constituting a “second hit”60 to the brain. Existing evidence suggests that the hippocampus and other medial temporal lobe structures that are integral to episodic learning and memory may be particularly susceptible to damage from diabetes61 as well as hearing impairment.62 Damage specific to the medial temporal lobe may explain why the combination of excessive hyperglycemia and hearing impairment was connected to poorer performance on learning and memory but not on other cognitive domains.Third, we found that excessive hyperglycemia was associated with poorer learning and memory only in the presence of hearing impairment. This contrasts with our recent study demonstrating that diabetes was related to significant cognitive decline and mild cognitive impairment.63 whereby tasks that require greater auditory load  may be more sensitive to hearing impairment. Among individuals classified as hyperglycemic and having hearing impairment, cognitive resources may be diverted away from cognition and toward more “effortful listening.”64 In addition, the only task that did not demonstrate a hearing impairment disadvantage was a nonverbal task of processing speed/executive functioning. Deal et al53 also were not able to detect hearing impairment-related differences in nonverbal  tasks, whereas disadvantages were observed on baseline memory. Still, others54 including Golub and colleagues22 found that hearing impairment was associated with poorer performance on nonverbal tasks . Unlike Golub and colleagues,22 we did not find a robust association between hearing impairment and processing speed/executive functioning, possibly owing to differences in CVDR measures. All cardiometabolic factors incorporated into our analyses were measured in the clinical setting, and we used a data-driven approach to classifying CVDR. In addition, we extended the previous findings by controlling for key sociodemographic factors that have been shown to be related to hearing impairment and cognition in this population .66Importantly, the relationships between hearing and cognition were not cognitive domain specific, yet the modification by CVDR was specific to learning and memory. The latter is somewhat consistent with the information-degradation hypothesis,A major strength of this study is extensive CVDR phenotyping among the most diverse cohort of Hispanic or Latino participants in the US. In addition, we replicated our findings using (1) a 3-class CVDR grouping and (2) only individuals who did not use hearing aids.68 Fourth, although we controlled for several demographic factors, we did not examine interactions between hearing impairment and such factors. Finally, both audiometry and cognitive testing were completed in quiet  environments, which enhance performance but may limit ecological validity.70The conclusions drawn from our study are limited by several factors. First, our data are cross-sectional. Therefore, we were unable to test cognitive decline, and we are unable to make causal inferences. Second, although the use of audiometry to measure hearing impairment is a strength, we lack data on duration or history of hearing impairment. Third, we did not incorporate data on social isolation or depression as potential mediators between hearing impairment and cognitive decline.In this study, hearing impairment was connected to poorer cognitive functioning among a representative sample of diverse middle-aged and older Hispanic or Latino participants. In addition, excessively high glucose was associated with poorer learning and memory only among individuals with hearing impairment. Our findings warrant longitudinal investigations into hearing impairment as a risk factor for cognitive decline and ADRD among diverse Hispanic or Latino patient populations.


This article presents the details and findings of a practical implementation of a preliminary model for use in hearing conservation programmes (HCPs) in the mining sector in South Africa.The implementation is based on a previously published model, called feedback-based noise monitoring model (FBNMM), and represents its implementation as a tool of predicting occupational noise-induced hearing loss (ONIHL), as well as monitoring and managing HCPs in the mining sector within the South African context.The article, utilising real miners’ data, demonstrates this basic static feedback model with its practical applications such as estimating, monitoring and providing quantitative information to aid miners, mining administrators and policymakers in decision-making around HCPs, as recommended in the previous 2019 publication by Moroe et al. This study was conducted in a South African platinum mine. Evidence on the model’s sensitivity and practicability in early identification, intervention and management of ONIHL in the workplace is presented.Findings show how the use of the model within an HCP viewed as a complex intervention can allow for early prediction of ONIHL, consequently affording more accurate early intervention as part of preventive audiology within the health and safety goals of mines.The feedback-based model should be a useful tool for successful implementation and monitoring of HCPs within South African mines. Occupational noise-induced hearing loss (ONIHL) is a complex disease , where effective ways of assessing, monitoring and reducing or eliminating excessive workplace exposure to hazardous noise are adhered to Amedofu, . EfficacT]he underlying premise of risk management is that improvements in health and safety can be made by correctly identifying and addressing hazards or factors (which may be underlying or direct) that contribute to occupational risk. (p. 536)[According to Madahana, Ekoru and Nyandoro :From The current model shown in Subsystem 1, which is also the baseline or the reference, is interpreted as the tracking unit. It is a non-constant reference. With sufficient hearing protection, the mine worker’s threshold shift is expected to closely follow the baseline. This would then be interpreted that the miner is only being affected by presbycusis and not occupational noise.Actual values are used in the input noise intensity that is a disturbance to the system. The exposure levels for each mine worker are identified according to the drilling machine that the mine worker operates.To simplify the developed current model, the risk treatment is represented by the actuator (hearing protection) and the plant (ISO equations that represent hearing loss). These are also considered to be on the forward path.Data used for model validation were collected from all the individuals who work at the platinum mine. However, based on key findings by Edwards and Kritzinger , when thThe current study therefore targeted one of the worst-case scenarios of mine employees. These were the workers who are affected the most by occupational noise because of the nature of their tasks in the mine. Edwards and Kritzinger  proposedIn this study, the two forms of hearing protection used by the rock drill mine operators are described below:3M peltor H9A optime 98 over-the-Head earmuff. Noise reduction rating (NRR)*: 25 dB. CSA Class A. *The NRR may overestimate the hearing protection provided during typical use. 3M recommends reducing the NRR by 50% for estimating the amount of noise reduction provided .The mine workers are not only exposed to drilling noise but also to the different sources of noise within the mine, for instance, from transportation.It is assumed that all rock drillers use the 18.35 dBA and 25 dBA attenuation setting provided and not the worst-case scenario of 7.1 dBA.The data set did not indicate the exact hearing protection that the mine worker uses; hence, both case scenarios are simulated (i.e. the 25 dBA and 18.35 dBA).Two hundred and ten rock drill operators employed in a platinum mine were chosen. Their occupational exposure histories were collected. Five variables which can influence hearing loss were used in the development of the predictive model.Ethical approval to conduct the study was obtained from the Human Research Ethics Committee of the University of the Witwatersrand  on 18 April 2016. Data used in the current study were obtained from a bigger study entitled ‘Occupational noise-induced hearing loss in South African large-scale mines: From policy formulation to implementation and monitoring’ Moroe, . The datThe presented results in The predictive tool using the provided inputs presents a futuristic estimation of how the thresholds will shift as shown in quiet level of machines they use as well as the age of the machines being used. Although these strategies would be important for all mine workers, current findings suggest more active and age-specific interventions, and hence the suggestions made for the older miner in this article.In It is observed in The FBNMM that was put into action in this article clearly provided a prediction of how a mining employee’s hearing function would present with given attenuation at speech frequencies. Suppose that this tool had been employed at the beginning of the employee’s career, then correct measure to ensure zero harm as required by the Mine Health and Safety Council would have been taken by the mine administrators in the performance of administrative controls and personal hearing protection pillars of their HCP. The FBNMM is significant to the HCP because it is a tool that can be used to predict hearing impairment and allow for provision of early intervention to prevent ONIHL  and HIV, the presence of dust, ototoxic chemicals and cigarette smoking, are considered and managed as part of the HCP. These factors, which were not added into the FBNMM as inputs, are argued as the possible explanation as to why there was an inability of the graphs to closely track (follow) the baseline irrespective of the hearing protection provided. Current implementation of the FBNMM reveals that, over and above what Edwards and Kritzinger  list as Putting the FBNMM into action in this article yielded positive findings that have important implications for HCPs. Furthermore, actionalising the FBNMM raised the following recommendations for future studies, which will be tested, documented and reported on an already planned follow-up paper:inclusion of more dynamic aspects to the system, for example, the use of an ear model of a human beinginclusion of other aspects that affect the mining environment, for instance, acoustic environment, temperature and ventilation in account during the period values were selected versus, how it would be in the future? Does it consider, for example, number of drills in operation at a time, or the acoustic environment variations machinery in operation, faults or dykes, water, etc.?inclusion of other risk factors, over and above age, gender, ethnicity, nutrition, exposure to chemicals (in the workplace as well as because of medication), family history of exposure and the likelihood of developing ONIHL at a faster rate once exposed previously that influence susceptibility of an individual to ONIHL in the modeldevelopment of a preliminary working prototype to be used in monitoring of ONIHL in the mineinclusion of artificial intelligence and machine learning concepts in the working prototype.The use of metrics, as was performed in the current study, is argued by the authors to be advancement in the management of ONIHL and has significant value towards efficacious implementation and monitoring of HCPs. The close link of metrics with artificial intelligence places this advancement at the forefront of audiology’s engagement with the Fourth Industrial Revolution in combating ONIHL.


The purpose of this study was to evaluate the association of hearing loss with atherogenic index (AI) in the general population.A multistage study using cluster random sampling method was conducted in the Zhejiang province from 2016 to 2018. Pure-tone air-conduction hearing thresholds were measured at frequencies of 0.125–8 kHz for each subject. After obtaining their consent, all participants were asked to provide their own plasma lipid data.A total of 3,414 eligible participants were included, 1,765 (51.7%) were men and 1,649 (48.3%) were women and 1,113 (32.6%) had hearing loss. Ridge regression showed increased AI in subjects with hearing loss. The subgroup with the highest quartile of AI, presenting the highest risk of hearing loss as compared to the lowest quartile, comprised young and middle-aged women. Further analysis revealed that the AI in people with different categories of hearing loss was higher than that in the normal population, except for those with (extremely) severe hearing loss. Moreover, the young and middle-aged women exhibited the most significant correlations between AI and hearing loss.AI was positively associated with hearing loss in the general population, especially the young and middle-aged women. AbundanIt has been shown that the ratio of non-high-density lipoprotein cholesterol  and HDL-C, known as the atherogenic index (AI) ,21,22, i22.1A multistage study using cluster random sampling method was conducted in the Zhejiang Province from September 2016 to November 2018, and the data from six health-care centers were collected and collated. These selected centers were as follows: one in Jiangshan, one in Jiaxing, one in Huzhou and three in Hangzhou . A total of 5,303 subjects agreed to provide the relevant information and consented to its use in the future research.The entire process of the study was approved by the Institutional Review Board of Hangzhou Normal University (grant number 2017LL107). All subjects provided written informed consent, and this research followed the tenets of the Declaration of Helsinki published in 1964 and its later amendments and also the local government policies.2.2n = 111). Furthermore, we excluded participants with missing information on lipid profiles including triglycerides , TC , LDL-C  and/or HDL-C , leaving 3,414 participants. All pure-tone air-conduction hearing thresholds were measured by trained researchers using audiometers  with supra-aural headphones . Each participant was evaluated for hearing thresholds between 0.125 and 8 kHz  over an intensity range of −10 to 110 dB in a soundproof booth with a background noise of less than 20 dB(A). All facilities were calibrated before use, and we conducted the procedure by beginning at 1 kHz, continuing to higher test frequencies and then returning to 1 kHz, followed by testing lower frequencies, and the same test method has been described in detail in previous study [K-mean cluster analysis: a flat shape, a 2–4 kHz abrupt loss (AL) shape and a sloping shape  or abnormal ear structure were excluded from the analysis  following an overnight fast and measured at the clinical laboratories of each health-care center. Due to inconsistencies in the items examined by each center, some plasma lipid indicators  were eventually not included in the analysis.2.3t test, χ2 test and Wilcoxon rank sum test were used to compare the differences between the groups, and the Bonferroni correction was applied for multiple comparison. Multicollinearity was assessed by computing the variance inflation factors (VIFs) to determine whether multiple linear regression or ridge regression analysis [K-mean cluster analysis method  was used to classify the patients’ audiogram shapes into several categories for analysis. Furthermore, the analysis of variance test and logistic regression were conducted to analyze the hearing threshold differences between different hearing loss levels and audiogram shapes, after adjusting (and/or stratification) for age and sex. To assess the consistency of the findings across participant characteristics, we estimated the odds ratio (OR) and 95% confidence interval (CI) of hearing loss for a doubling in AI concentration [All raw data were documented and verified  via EpiData 3.1 . The statistical package for the social sciences (SPSS) software  was used to conduct all statistical analyses, and the results were graphically represented using the Adobe Illustrator CS5 software . Data were presented as mean ± standard deviation or median (interquartile range) according to the distribution of each variable as examined by the Kolmogorov–Smirnov normality test. Student’s analysis ,32 is usntration ,34. All 33.1p = 0.041), TC ( p < 0.001), LDL-C ( p < 0.001) levels and AI ( p < 0.001) were significantly higher, whereas the HDL-C level ( p = 0.022) was significantly lower in the hearing loss group.Of the 3,414 eligible participants, 1,765 (51.7%) were men and 1,649 (48.3%) were women, and a total of 1,113 (32.6%) had hearing loss. 3.2B = 0.20, p = 0.007; Table S1). Furthermore, when stratified by sex and age , presented the highest risk of hearing loss when compared to those with the lowest quartile.Since the relevant plasma lipid indexes and the hearing thresholds were collected as quantitative data, and a high correlation existing among the independent variables , ridge regression is considered an appropriate method to analyze the relationship between PTA and plasma lipid indexes. In addition to age and sex as the indicators of lipid parameters, AI showed significant positive correlation with PTA (3.3n = 191), a 2–4 kHz AL shape (n = 256), and a sloping shape (n = 666). Table S2 presents the estimated changes in risk associated with AI levels in different hearing groups through different adjusted covariates. Without adjustment for age and sex, the per doubling increase in AI was significantly associated with a 62%, 50% and 32% increase in the risk of hearing loss , respectively (Table S2). Furthermore, based on an in-depth analysis, after stratifying by sex and age . However, the OR between the flat shape subgroup and the normal group failed to achieve statistical significance.According to Section  and age , increasn = 2,301), mild (n = 849), moderate (n = 224) and (extremely) severe . As shown in p < 0.001). Additionally, with progressive increase in the degree of hearing loss, the AI gradually decreased, and no significant difference was observed between the (extremely) severe subgroup and the normal group (p = 0.897). The results, after stratification for sex and age  of the hearing loss population are the contents of this paper. Through appropriate statistical analysis methods, the relationship between AI and different hearing losses in different groups is discussed in detail. It is hoped that some research results in this study could provide new ideas or directions for future study on hearing loss.Different from other studies ,16, the in vivo imbalance of oxidation/antioxidation [Due to the existence of multicollinearity among the independent variables, the precondition for applying multiple linear regression was not met. Herein, the ridge regression analysis, a specialized processing statistical analysis that deals with multicollinearity problem, is a suitable alternative to assess the correlation. Ridge regression results showed that the relationship between single plasma lipid indicator and hearing loss was not significant; however, the comprehensive indicator, AI, significantly correlated with hearing loss. The above results were partially consistent with previous studies ,15, wherxidation ,45) may n = 40).The patients with hearing loss were grouped according to the different criteria for further analyses. The differences in audiogram shapes of people with hearing loss introduce no change in the results, since the overall AI values in the hearing loss group were higher than that in the normal group. Additionally, compared with the normal group, after adjusting for age and sex, AI was significantly different in the mild hearing loss subgroup but not in the (extremely) severe subgroup . The resn = 40), and subsequently, poststratification, the number of participants will be even smaller.On the contrary, in addition to adjusting for sex and age, our study found that young and middle-aged women were more sensitive to AI changes through stratified analysis . This reThe limitations of this study need to be noted. First, a causal relationship could not be established due to the cross-sectional study nature. Second, this study did not exclude people with hearing loss caused by ototoxic drugs or noise trauma, as these data were not collected. Third, plasma lipid data from different regions were measured in the clinical laboratories of the respective health-care centers, therefore, the possibility of detection bias cannot be excluded. Fourth, because the parameters including lifestyle factors (such as exercise and diet), genetic factors, systolic and diastolic blood pressure, apolipoprotein A1 and other physiological and biochemical indicators were missing or not examined, the aforementioned factors could not be included in the analyses.5In this population-based cross-sectional study which analyzed various variables and quantitative data, we found AI to be positively associated with hearing loss in the general population, especially, the young and middle-aged women. These results may provide clues to delineate the mechanism of hearing loss. Nevertheless, possibly the most critical for public health is that elevated AI can elicit adverse effects on hearing even at levels well below the critical value (<4), which increase the importance of maintaining the results that suggest low level of AI.