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- [00:05]
A
Bovine leucosis. We've talked about it in dairies. How does it affect our cow calf herds? And are there implications for some of the things we really care about like reproduction, culling, maybe even weaning weight of the calves? We'll find out today. This is after the Abstract on bovine science with BCI and I'm Brad White, joined by Dr. Todd Gunderson. Hey, Todd.
- [00:26]
B
Hey, Brad. How's it going?
- [00:27]
A
Good. I appreciate you bringing this article to our attention. This is a really recent article that you found we're going to Discuss. It's a 2026 published in Preventative Veterinary Medicine with the title High Prevalence of Bovine Leukemia Virus in Cow Calf Operations Implications for Cow Reproduction, Culling and Calf Weight with the lead author Sito, and the last author is Lammers. So this article drew your attention. I know you've got an interest in kind of the epidemiology of some of our diseases. And on the beef side, what specifically drew you to this article, Todd?
- [01:02]
B
Well, we often think of bovine leukemia virus as being a risk factor for disease and poor production. When I was coming up, in fact, I cut my teeth in veterinary medicine. In the dairy industry, we would often use bovine leukemia virus status as a guidepost for whether to cull a cow that was not doing well or not. And so there's been some other studies that have been published recently, some by researchers here at Kansas State where they looked at whether bovine leukemia virus status is a risk factor for poor production in beef cattle. And some of that data or some of that research suggests that maybe it's not as good of an indicator as we think. And so when I saw this study, which answers a lot of the same questions that were asked, and the study that was done here at K State, it really piqued my interest because I wanted to see if they saw similar results to what was found here.
- [01:56]
A
Yeah, that study here at K State, Dr. Larson and Dr. Hoosier did that work and they looked at some of the herds. So this has a little bit different impact. So when you look at this and we review and we go through our typical process, we'll review the abstract, we'll look at some tables and figures, talk about maybe how they did it, some of the specifics of the statistics and then our overall conclusions. What are some of the things you noticed in the abstract?
- [02:21]
B
Well, number one, this is a longitudinal study which makes it kind of nice as a follow up study to the studies that were done here by Dr. Larson, Dr. Huser and their collaborators and their study was a cross sectional study. So having a longitudinal study allows us to establish some sense of temporality between risk factors and measured outcomes. Another thing that stood out to me is that they were looking not only at bovine leukosis virus using a serum elisa, so they're looking for antibodies against the leucosis virus. They're also looking at proviral load, which is essentially a ratio of the viral DNA to the host DNA that is detected using polymerase chain reaction. And the proviral load is kind of a more recent metric that we think of as a more maybe specific finding, a more specific metric for certain, for certain impacts that bovine leukosis virus can have. So the fact that they looked at both ELISA and proviral load is exciting because it gives us some really good data to look at. The three outcomes that they were really looking at in these cows was pregnancy risk, culling risk and weaning weight. And then the other thing that kind of stood out to me was that the prevalence of bovine leukosis virus in this population, or at least of, of having a positive ELISA was pretty high. About 80% of cattle and 80% of herds had positive serum ELISA to bovine leukosis virus. Compare that to the Kansas study, which had a 55% cow level prevalence but a 95% herd level prevalence.
- [03:58]
A
So quite a high prevalence in this population. Sounds like a good population to study. The other thing I'll mention is I really like the way you think, Todd, because you, you read this and in the margins here, you've written on the proviral load that they reported an average in the abstract, but earlier in the abstract they said their analysis on provile load was done by categories that they had created. So the question there is if the number is skewed, if those results are skewed towards one direction or the other, categorizing it makes sense. Sense for the analysis.
- [04:34]
B
Right.
- [04:34]
A
However, if the number's skewed, I might want to report a median instead of a mean, which we'll get into as we go forward. But I just, I like that you're catching those sorts of things as you look in the abstract. And when they did this study, what were some of the preliminary results that they found?
- [04:51]
B
Well, mostly it's the biggest story here is what they didn't find. So they did not detect a significant association between ELISA results and pregnancy, colon or calf weaning was weight. So no real associations there. And we'll look at some of those figures and tables Here in a bit. The one thing they did find was that cows in the high proviral load category had a lower pregnancy risk than those in the very low proviral load category. And I'll save us some time and go ahead and give you a sneak preview here. I found out in the materials and methods that very low viral loads essentially means they did not detect any virus, any virus DNA using the pcr. So it's a proviral load of essentially zero. So compared to having a zero proviral load, the high proviral load category cows had a lower pregnancy risk, but they did not see an association between proviral load category with culling risk survival or weaning weight. They did do some Kaplan Meier survival curves that we'll look at here in a bit. So that's kind of interesting. And then there's one more preliminary result that I think we need to spend a little bit more time talking about. And that was they claimed that the proviral load decreased for 72% of the cows. And we'll talk about this. You have to be really careful in how you interpret that number. And we'll talk more about that here in a bit. But that was the other finding that they touted in the abstract and they talk about it quite a bit in the discussion and in the conclusions. It seemed that the proviral load risk decreased for a significant portion of the cows.
- [06:25]
A
And we'll flag that to talk about as we get to those specific results. The first figure and the first table I think pretty well outlined. I like the little infographic that they have of the timing of when they did the testing and when they did the follow up. And then in Table 1, they described their reasons for culling. And about 70% of the cows in these herds were culled due to reproduction, about 24% due to health reasons, which is most of the calls. But then if I flip to figure two, you've kind of outlined some of that. But what did you see in figure two?
- [07:05]
B
Well, in figure two we're looking at the prevalence of ELISA by season. I do want to point out one quick thing though from Table 1 before we get into Figure 2. They show that that's the number of cows that were culled. Out of all the cows that were enrolled in the study, it turned out to be about 21, 22% of the cows. Over three years, that comes out to about a 7% incidence culling per year. Just wanted to clarify that on Figure 2. What, what stands out to me is this Is the, the basically the prevalence of bvd, or excuse me, a bovine leukosis virus based on ELISA results across the different bleeding. So fall 2021, spring 2022, that's bleeding one, because some of these cows were fall calving herds, some were spring calving herds. So those two are bleeding. Are the prevalence from bleeding one, fall 2022, spring 2023, that's the prevalence from bleeding too. So we have two main collection, data collection events. The prevalence changes slightly, though this is mostly descriptive.
- [08:13]
A
So that tells us one pretty high prevalence across the board, which you said from the abstract. And then figure three actually answers your question on what are those proviral loads distributed like? And I like this kind of breakout. What do we see in that figure?
- [08:30]
B
So here we have the proviral load between the two different bleedings. And what you can see here is that both of these distributions are pretty right skewed. Like we have a large proportion of cows that had zero proviral load and then it kind of tails out to the right. And so definitely this is not normally distributed data. I think it would have been more appropriate to have reported the proviral load as a median value rather than a mean. And they do, in the tables they do show the median and the mean. So you can see there's quite a bit of separation between the median and the mean. So I think, I think a median would have been a more appropriate. I think ultimately they handled these data correctly. They did the right statistical analysis with these, given the type of distribution that we have. Just from a, I guess, statistical etiquette standpoint, I probably would have reported that value as a median rather than a mean in the abstract, in the abstract.
- [09:22]
A
But essentially many cows are zero or near zero and there's a few that skew out there to the right where they have high proviral loads. And then they also described in table to some of the, some of the pregnancy rates. What did you see there?
- [09:40]
B
So in Table 2, we're seeing that the pregnancy risk across the different bleedings, different seasons was in the kind of the 90% overall pregnancy risk for all cows that were evaluated. Now, the overall cull rate was about 7% and there were 10% roughly that were open every year. So that tells me this is kind of an aside thing, but it tells me that not every open cow was cold and the pregnancy evaluation was done at different times for different groups of cows. So if you were in the fall group, the sampling for BLV was done at preg check, while if you were in the spring group. The sampling was actually done during synchronization for artificial insemination. So a little bit different timing for when they sampled these cows to take their BLV status versus when they did the pregnancy diagnosis. But overall, I mean, this looks like these cattle are fairly representative of what I would expect for typical cattle in North America.
- [10:40]
A
Yeah. And when they break it out by Eliza's status, which you already said in the abstract, there wasn't an association here. But when they combine everything, the pregnancy rate for pregnancy risk for those that were positive was 89.2, and those that were negative was 90.5. So very, very, very close.
- [11:01]
B
Yeah, they're pretty close.
- [11:03]
A
So then as we move to table 3, what do you see there?
- [11:09]
B
All right, so this is where we start to get into the really juicy part of the paper. This is where we're looking at our statistical models, our inferential stats. And this is the model that looked at pregnancy risk associated with ELISA status. And what you see, the first thing is that Eliza status was not associated. As you mentioned from the previous table, there was a significant association between breeding age and pregnancy risk, which we would expect. And the way it works in a logistic regression, when you have a continuous explanatory variable like breeding age, they break it up into units. So for every one year increase in breeding age, there is a decrease in the risk of cows begin pregnant. And that makes sense. You would expect that. And so controlling for breeding age, controlling for body condition score, and controlling for breeding season, they did not see an association between Elijah status and pregnancy risk. And you might ask, well, why did they decide to include all of those different variables in this model, especially if breeding season and body condition score weren't significant? And they go into that a little bit in their materials and methods. I don't want to spend a lot of time on it, but essentially they looked at certain variables that they felt had a large potential influence on pregnancy risk, and they felt like they should be in the model based on some of the clustering analysis they did. And that's why those variables are kept in the model even if they aren't statistically significant in the multivariable model.
- [12:43]
A
So that tells us a little bit about just ELISA status, which doesn't tie in this provable load.
- [12:50]
B
Right.
- [12:50]
A
But Tables 4, 5, and 6 address that proviral load in a little bit different way. What did you get from those?
- [12:59]
B
Okay, so we do see in Table 4 that there is a significant difference between the high proviral load category cattle and the very low proviral category cattle. Now something, I will point out these tables, and this was just something that happens with public publishing these papers. There's a little bit of a formatting error and you kind of have to notice it in order to understand these numbers really well. If you notice in the 95% confidence interval column, and this is the same in all these tables, all those confidence intervals are shifted one line up from where they should be. And I think that was just a minor formatting error. But like the 95% confidence interval for the high category for proviral load was 0.35 to 0.99. And what you notice about that is it's that entire range in the confidence interval is less than 1. And that's when we're looking at odds ratios. That's one way that you can see that there's a meaningful difference there. If the confidence interval around that odds ratio is either completely less than one or completely greater than one, if it doesn't cross one, that's a sign that that is an actual risk factor. So here we see that the high category proviral load cattle had slightly lower odds of being pregnant than the very low category cattle. But that's the only, that's the only comparison where that was true. Otherwise, breeding season, body condition score were not significantly associated with pregnancy risk. Breeding age once again was which we would expect. However, that's just the variables they left in there to control for those particular aspects. So they could feel like they were isolating specifically the effective proviral load category.
- [14:43]
A
Yeah. And so the pregnancy. And they break that out. And maybe worth jumping to figure four because figure four kind of breaks that out graphically. And when we say the risk was the risk of pregnancy, meaning less pregnancies in that high proviral load category basically compared to the very low category is the only statistical difference. And I don't know the exact numbers, but looking at the table, it's about 92% versus 93%. Right. So yes, statistically different. But you're not talking about a huge drop off there. Sometimes when we talk about those, you think about, oh, it's a big difference, but that's right. Not a huge drop off. And then they, they had some. Had some other comparisons and, and I may, just as we think through the. The sake of time here, were there any other big take homes before we jump back a little bit? Because I wanted to get to figure seven.
- [15:45]
B
Yeah, yeah, I want to get to figure seven as well. Basically. No, I really liked figure four. Now you're right, I kind of, I was kind of interpreting Table 4 using information that was presented in Figure 4. So I apologize for that. But you're right. That's where we actually can see the pairwise comparisons. There's no other big take homes. They did not find a difference in culling risk. They didn't find a difference in culling risk with either ELISA or proviral load category. That was Tables 5 and 6. You know, Figure 5, they show the survival curves. Interestingly, they did not see a difference in the proportion of cows cold using survival curves for either ELISA status or proviral load. And that's significant because if you were going to see a difference, you'd be more likely to see it in the survival curve. That's a little bit more sensitive of a statistical test to those differences. They did not see those differences. So that was interesting.
- [16:40]
A
So not only the magnitude but the timing, they didn't find differences.
- [16:44]
B
Correct? That's right. And then, you know, figure six. I wonder, I kind of wondered why they put figure six in there other than it's kind of interesting to see how they weaning weight categories based on proviral load were like almost identical across all four categories. I thought that was interesting. But in the sake of time, let's, let's, let's skip on to figure seven. I want to talk about figure seven a little bit because figure seven, it's a neat graphic. It's a heat map. And what it does is it shows all of those cows who, from whom they were able to get a proviral load category measurement in both bleeding. So the, the first bleeding, that was the spring fall 20, 21, 22 versus the second bleeding which was the spring fall 2022, 2023. And they compare the category status of those cattle between those two time points. This is kind of like a repeated measure. And what this heat map does is it shows you if a cow's proviral load status either stayed the same, which is the diagonal running from the top left to the bottom right, whether they actually decreased in their proviral load status. So that would be everything at the bottom left half of the square or if their proviral load status increased, which was the top half. And the abstract, they report that 72% of cows decreased their proviral load status from the 1 measurement to the next. There's a little bit of a nuance there though that you need to be aware of. And that is cows whose proviral load status changed. There were 395 cows whose proviral load status changed from 1 bleeding to the next. And of those whose status changed 72%, their proviral load status decreased. But if you account for the cows whose proviral load status stayed the same, you put those cows in the denominator. Well, now it turns out that only about 26% of cows proviral load status decreased from one bleeding to the next 74%. Either their proviral load status stayed the same or it increased the denominator.
- [18:55]
A
The denominators can be troublesome. But when you put it on terms of what happened across all cows.
- [19:03]
B
Right.
- [19:04]
A
The majority of them actually stayed the same category. Correct. So then that influences. If I only look at cows that moved, there were some that increased. But if I look at of all cows, that number that increased became a different number. So give me, if I'm a practitioner, I'm working with cow calf herds and overall a good paper. You and I had some questions as we discussed relative to the statistics, being sure that it looks like in most of their models they did a really good job. That final one that we were discussing, one of the questions is did we account for the herd effect? Because truly those cows aren't individual animals. They're individual animals embedded within a herd. But. Right, give me a, give me a take home or two for the someone reading this article of what might I do different? What might I consider?
- [19:58]
B
I think the take home here is that if I go into a herd of cows and everything is otherwise normal, I'm not seeing any evidence that we have a real problem with leukosis, not bovine leukosis virus. I'm talking about actual leukosis or bovine
- [20:14]
A
leukemia where I've got separating the positivity to the virus from the disease.
- [20:19]
B
That's right. That's right. Because the disease itself has various characteristic clinical signs. You'll have enlarged lymph nodes, you'll have cows that are poor doing. You'll have cows that on postmortem you actually find leukemia in the right heart, the uterus, the lymph nodes, the abomination, the spine. You find, you find that actual cancer. That's one thing. All right, we see that. That's one thing. But if I go into a herd that's otherwise doing fine and these herds appear to be normal otherwise, and I start doing a bunch of leukosis testing, I need to be very careful how I interpret these results because the results from this study and from the study that was done here at K State Tell me that. The leucosis status on an ELISA tells me virtually nothing about that cow's risk for production loss culling, any of it. And this study, other than the one difference between the high proviral load and the not being able to detect any virus category, this study tells me that I really can't say much based on the proviral load either.
- [21:28]
A
And keeping in perspective that distribution of proviral loads, very few of them have high proviral loads, correct?
- [21:35]
B
Yeah, it's a very small proportion that have very high proviral loads. And you know, they broke proviral load out into four categories based on quartiles. So not completely arbitrary, but definitely not based on some well known biological cut point either. And so it's somewhat arbitrary. A very high proviral load versus no proviral load. But is proviral load of slightly more useful metric than ELISA status? Yeah, probably in some circumstances. Is it the end all be all that's going to tell me everything I need to know about what's going to happen with this herd from a production standpoint, from a disease standpoint, I don't think it's that good, I really don't. And so you have to be careful drawing conclusions from these ELISA tests and these proviral load tests. Cows that are otherwise doing fine. Now if I go into a herd of any herd of cows, dairy or beef, and I see a cow, she's got swollen lymph nodes, you know, she's chronically wasting away, she's not doing well. I do have BLV test on her and she's positive either on an ELIZA or has a high proviral load. Yeah, I'm probably going to diagnose her as leukosis and her prognosis is poor. And that, that, that test would probably confirm my preclinical suspicion. I might not even need to do the test in that situation. But you know, that's, that's one area where I feel like I can trust the results of the test. What this paper shows me and what other papers along the same lines is that in Beefords doing the ELISA for bovine leukosis virus has very limited utility and the proviral load may be slightly marginally better utility but not, not that great either.
- [23:14]
A
Yeah, so I think excellent conclusions there. And this is the second paper that's shown not a great association between the, well, you don't see culling risk or pregnancy status associated with the ELISA test. You did highlight the proviral lobe, but also this paper tied in weaning weight, which I thought was good. So great paper. Thanks for sharing this with us. If you have a paper you'd like us to talk about on after the abstract, you can send us an email at bciasu Edu to the break of dawn.