Mystery Rays from Outer Space

Highly pathogenic H5N1 influenza viruses possessing mammalian-type PB2-627 were detected during the Qinghai Lake outbreak in 2005 and spread to Europe and Africa. …  Here, we report that H5N1 avian influenza viruses possessing mammalian-type amino acids in PB2-627 or -701 are selected during replication in ostrich cells in vitro and in vivo.

—Shinya, K., Makino, A., Ozawa, M., Kim, J., Sakai-Tagawa, Y., Ito, M., Le, Q., & Kawaoka, Y. (2009). Ostrich involvement in the selection of H5N1 influenza virus possessing mammalian-type amino acids in PB2 Journal of Virology DOI: 10.1128/JVI.01714-09

My friend Lauredhel, at the Hoyden About Town blog, made an interesting point about risk factors for swine-origin influenza virus (SOIV), and the perception of those risk factors in the press.   The press has made a big deal of the putative link between obesity and risk of severe SOIV.  But, as she pointed out back in June, the data¹ at that point showed no such link — in fact the percent of obese people with severe SOIV was if anything lower than the frequency of obesity in the general population.  However, the press picked up on this because of a reply to a question at the May CDC press briefing, and the headlines were all over this (actually non-existent) link.

Lauredhel made the same point when a recent paper in the Medical Journal of Australia reviewed cases of SOIV. ²  Once again, Lauredhel points out, in this series of patients, obesity was actually lower in severely affected people than in the general population:

Around 18% of the Australian population is obese. Around 7% of people severely ill with H1N1 flu are obese. ³

However, I don’t think the story is that simple.  I made a few comments there, which I’ll review here.  The summary of the exchange, I think (Lauredhel might disagree) is that some, but not all, studies have found a connection between obesity and risk of severe SOIV; the largest studies do show a connection, but when looking at the overall picture, it’s not a strong connection.  The press has, however, vastly overstated this link, focusing on it apparently because of one comment in a press briefing, but ignoring several attempts to clarify and downplay the observation.

My comments:

The largest series I’m aware of is the recent JAMA study. ⁴ (The eMJA paper came about about simultaneously, so they can be forgiven for claiming theirs is the largest study.) Here 48% (of hospitalized or fatal cases, where data were recorded) were obese. The rate was slightly higher in fatal cases (66%, of 110 cases total) than non-fatal (52%, of 212 cases; these are cases over 18 years, and the non-fatal rate would be lower if we included younger people). The fatal cases, especially, were disproportionately in the highest BMI cases — 50% of fatal cases had a BMI over 40, if I’m reading their Table 2 right.

I don’t know what the relevant population rates of obesity are, so we don’t know relative risk. In the US generally, I believe obesity is in the 30% range. The authors say “Of adults with BMI data available, more than half were obese and one-quarter were morbidly obese. As a point of reference, the percentage of adults who are morbidly obese in the United States is 4.8%”.⁵

(An important concern is that this may be distorted. It looks as if data weren’t recorded for obesity on the majority of patients. I would worry about a recording bias, with information on obese patients being recorded more readily than for non-obese. Still, even if not one of the non-recorded patients had BMI over 40, the case rate is higher than the 4.8% background.)

In smaller studies, there seems to be a similar picture. In a Michigan survey (June ‘09) 9 of the 10 patients with swine-origin H1N1 hospitalized with ARDS were obese; in a European survey, 8 of 13 fatal cases were obese.

The numbers are still quite small, and they’re not all consistent, but from what I see here, I wouldn’t dismiss obesity as a risk factor.

Also, I see a survey in Australia⁶ where the relative risk of obesity and “morbid obesity” (BMI > 40) is worked out. Just as you note, the relative risk of “obesity” for death is less than 1 (0.6), but probably 1 is easily in the 95% confidence interval. But the RR for obesity of ICU admission is up (1.7) and for morbid obesity is 4.4; death RR for the latter is 2.4.

These data aren’t entirely consistent with the eMJA data, but I don’t have time to try to resolve the differences. One point that’s raised by the JAMA study I quoted above is whether BMI is specifically recorded in these cases. The eMJA study only notes 8 patients that were obese, but unlike the JAMA study they don’t explicitly give a denominator — that is, they don’t specifically say that the remaining 104 patients had a BMI recorded at all. Could the BMI simply not be available for some of these remaining patients? I don’t know, I’m asking.

Lauredhel said in the comments:

I saw the CDC and other ‘experts’ at the beginning deciding that obesity was obviously a major risk factor based on early data that appeared to show the complete opposite

I replied:

For what it’s worth, neither the CDC MMWR article from May, nor the eMJA paper that just came out,⁷ say that obesity is a factor. The CDC report includes obesity last in a list of underlying medical conditions, and never says that it’s a risk factor per se. The eMJA paper only mentions obesity when they define it, show the rate in a table, and don’t mention it in their discussion at all.

So I’m not sure you can blame the scientists here. The overwrought press coverage, as far as I can see, entirely arose out of a comment by Anne Schuchat in a press briefing. (Schuchat would not be one of the authors of the article.). Her comment certainly was misleading, but it’s not quoting the scientists who did the work; it seems to come out of nowhere. Not excusing her here, but I would bet her comment was in response to a specific question from the press, not something she raised herself, and she seems to have only been referring to “severe cases” (not all the cases in that MMWR report), which at that time would have been a tiny subset of a tiny subset of cases.

And she corrected herself, at least partially, later on. If you look at a subsequent press briefing (in July) she specifically says that the difference is not there, especially accounting for other underlying conditions, and “They [obese people] would not be a targeted group.” That didn’t get any press, as far as I can find.

So, not surprisingly, the press has done a poor job of covering this, jumping on the comment from Schuchat without checking the figures. The experts, at least those who are actually doing the work, aren’t making the connection except in those studies that actually do see a disproportionate number (the JAMA study and others).

Lauredhel said:

Schuchat’s original statements weren’t a single throwaway remark

I replied:

That’s true, but in fact the context of her explanation was exactly the point you are making — that the frequency of obesity in SOIV patients wasn’t necessarily higher than that in the population (”So it’s hard for us to say at this point to say whether the number of patients with reported obesity is significantly higher than we would expect”). In other words, I’d say that “her perceptions of obesity and risk” were pretty much what you’re saying.

That was the May press briefing, the one that led to the press rampage. Now, reading the press conference transcript, the point she tried to make didn’t come across very well, because she started off sounding as if she agreed with the obesity issue and didn’t make the qualifications until several questions later. She clearly recognized that she hadn’t been clear, because she tried to clarify the point in each of the subsequent briefings (in June and in July). But I’m not seeing this as the experts making assumptions — quite the opposite, in fact. The expert was carefully not making the assumption, but the press didn’t pick up on the qualifiers that she explicitly presented. She could have presented this better, but I’m inclined to put it down to imperfect communication, not jumping to conclusions.

1. Centers for Disease Control and Prevention (CDC) (2009). Hospitalized patients with novel influenza A (H1N1) virus infection – California, April-May, 2009. MMWR. Morbidity and mortality weekly report, 58 (19), 536-41 PMID: 19478723[↩]
2. Justin T Denholm, Claire L Gordon, Paul D Johnson, Saliya S Hewagama, Rhonda L Stuart, Craig Aboltins, Cameron Jeremiah, James Knox, Garry P Lane, Adrian R Tramontana, Monica A Slavin, Thomas R Schulz, Michael Richards, Chris J Birch, & Allen C Cheng (2010). Hospitalised adult patients with pandemic (H1N1) 2009 influenza in Melbourne, Australia The Medical Journal of Australia, 192, 1-3[↩]
3. Obesity Still Dramatically Decreases Risk of Severe H1N1 Flu?[↩]
4. Louie JK, Acosta M, Winter K, Jean C, Gavali S, Schechter R, Vugia D, Harriman K, Matyas B, Glaser CA, Samuel MC, Rosenberg J, Talarico J, Hatch D, & California Pandemic (H1N1) Working Group (2009). Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA : the journal of the American Medical Association, 302 (17), 1896-902 PMID: 19887665[↩]
5. Louie JK, Acosta M, Winter K, Jean C, Gavali S, Schechter R, Vugia D, Harriman K, Matyas B, Glaser CA, Samuel MC, Rosenberg J, Talarico J, Hatch D, & California Pandemic (H1N1) Working Group (2009). Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA : the journal of the American Medical Association, 302 (17), 1896-902 PMID: 19887665[↩]
6. New South Wales public health network (2009). Progression and impact of the first winter wave of the 2009 pandemic H1N1 influenza in New South Wales, Australia. Euro surveillance : bulletin europeen sur les maladies transmissibles = European communicable disease bulletin, 14 (42) PMID: 19883546[↩]
7. Justin T Denholm, Claire L Gordon, Paul D Johnson, Saliya S Hewagama, Rhonda L Stuart, Craig Aboltins, Cameron Jeremiah, James Knox, Garry P Lane, Adrian R Tramontana, Monica A Slavin, Thomas R Schulz, Michael Richards, Chris J Birch, & Allen C Cheng (2010). Hospitalised adult patients with pandemic (H1N1) 2009 influenza in Melbourne, Australia The Medical Journal of Australia, 192, 1-3[↩]

The number of influenza cases this year seems to have peaked and started to drop in the last few weeks, according to both the CDC surveillance data and Google Flu Trends (which updates more in real-time).  Does that mean swine-origin influenza virus is gone for good? We don’t know, of course, but I was struck by the resemblance of this year’s caseload to charts I’ve seen of the 1918 influenza mortality rates:

I don’t have the original data that were used to make the 1918 data¹ (black traces; these are mortality data, rather than cases; some sources suggest that this would underestimate the case number for the spring/summer peak of 1918, if the virus increased its virulence before the fall outbreak) , but I think I have the chart aligned with the CDC’s weeklies (red) for this year’s pandemic. ² They’re not identical, but they’re similar so far.  In particular, there was a surge in the summer, a big peak early in the fall — much earlier than standard seasonal influenza, which doesn’t usually get going until January or February — and then the fall cases dropped dramatically.  The 1918 influenza was followed by a third, smaller, peak, in winter, around the usual period for influenza.

In 1957, when a new pandemic influenza struck the US, the pattern of three waves — smallish in summer, very large in fall, followed by a slightly smaller but still major wave in early winter — was also broadly similar.³ Again, there was a spring/summer wave throughout the US, starting in June and peaking, maybe, in August.⁴  The fall outbreak started in September and peaked in late October, there was a lull, and then there was a new surge early in 1958, this time perhaps peaking a little later in the winter than the 1918 version.

1957/1958 influenza cases, by week5

The next pandemic was 1968/1969, when H3N2 moved in and supplanted the H2N2.  The pattern here seems different: There was little or no spring/summer wave,⁶ and while the outbreak did start a little earlier than usual, it was only by a few weeks:

The first outbreaks in the civilian population developed in Puerto Rico and Alaska in late September and early October. The first outbreak in  civilian population in the continental USA did not develop until the third week of October, when the small desert city of Needles, Calif., reported an influenza-like illness involving 35%-40% of the of the population. ⁷

(By comparison, by the third week in October this year, the pandemic seems to have just about peaked.)  Along with the late start in 1968, there was no lull; the influenza kept building to its peak, in December, and then quickly dropped down and stayed down. It was really more like a very large seasonal epidemic, very different from the patterns of 1918, 1957, or 2009.  (There’s actually a more impressive bump in the following summer, 1969, so this might be simply because the virus didn’t reach the US in time, and carried over into the summer rather than presaging the pandemic.)

1968/1969 influenza cases, by week, compared to previous years7

The next pandemic was 1977-1978, when H1N1 returned.  I don’t think we have good data for that, because the main measure was death, and that pandemic strain didn’t impact mortality significantly. ^{8 ,9}  However, as far as I can tell, that was mainly a winter-only wave. So the common claim that pandemics come in several waves is not universally true — just two of the four 20th century pandemics acted that way — but it does seem to be true for the 2009 pandemic.

In any case, based on previous pandemic patterns where the disease started this early, I’m guessing that we’re already past the very worst of the 2009 SOIV outbreak, and we’re going to enter a bit of a pause; but it’s going to come back early in 2010 — perhaps not to the same levels as we’ve been seeing in late October/early November, but not too far off.

1. Taubenberger JK, & Morens DM (2006). 1918 Influenza: the mother of all pandemics. Emerging infectious diseases, 12 (1), 15-22 PMID: 16494711[↩]
2. http://www.cdc.gov/flu/weekly/index.htm[↩]
3. This was the introduction of an H2N2 strain that replaced the previously-circulating H1N1 virus[↩]
4. I haven’t seen the data for the summer of 1957, so this is based on comments in the descriptive papers, and I don’t know how big the summer wave was[↩]
5. D. A. Henderson, Brooke Courtney, Thomas V. Inglesby, Eric Toner, & Jennifer B. Nuzzo (2009). Public Health and Medical Responses to the 1957-58 Influenza Pandemic Biosecurity and Bioterrorism, 7 (3), 1-9[↩]
6. You can see a little bump in the summer of 1968, which could conceivably have been the summer wave, but it’s definitely much less dramatic than the 1957 or the 2009 summer.[↩]
7. Robert G. Sharrar (1969). National Influenza Experience in the USA, 1968-69 Bull Wld Hlth Org, 41, 361-366[↩][↩]
8. Lui, K., & Kendal, A. (1987). Impact of influenza epidemics on mortality in the United States from October 1972 to May 1985. American Journal of Public Health, 77 (6), 712-716 DOI: 10.2105/AJPH.77.6.712[↩]
9. The 1977-78 H1N1 was almost certainly a laboratory accident, a release of an earlier strain from pre-1957.  Accordingly, most people over their mid-20s were already immune to the pandemic virus.  Since older people are usually the ones with the highest mortality rates, the 1977-78 pandemic didn’t trtanslate into increased mortality, and doesn’t show up in mortality rates.[↩]

—Patterson KD, & Pyle GF (1991). The geography and mortality of the 1918 influenza pandemic. Bulletin of the history of medicine, 65 (1), 4-21 PMID: 2021692

(Click on the image for a larger version)

Persons who were born before 1957 had a reduced risk of infection …  Persons who were born between 1957 and 1975 were at intermediate risk for infection. ¹

In Ontario, people over 53 years old had about 1/6 the chance² of getting the new H1N1; the those between about 33 and 53 had a little more than half the chance (odds ratios of about .15 and .42, respectively).

1. David N. Fisman, Rachel Savage, Jonathan Gubbay, Camille Achonu, Holy Akwar, David J. Farrell, Natasha S. Crowcroft, & Phil Jackson (2009). Older Age and a Reduced Likelihood of 2009 H1N1 Virus Infection The New England Journal of Medicine, 361, 2000-20001[↩]
2. I realize that odds ratios don’t quite say this, but it’s close enough[↩]

Apple’s iWorks ’06 package was interesting, but ended up being too simplified to really compete with MS Office.  But iWorks ’09 was a big step forward, and I now use Pages for almost all my word processing, and Numbers for about 75% of my spreadsheets.  (I still use Powerpoint for most of my slideshows; I don’t find any compelling reason to use Keynote instead, and Powerpoint does have some distinct advantages.)

“Numbers” looks fairly similar to Excel — they are both spreadsheet programs, so there’s only so many ways of usefully presenting information there — but the editing and so on can be quite different from Excel, which can be frustrating if you’re coming from an Excel background.  Rosie Redfield was just complaining about the non-intuitiveness of Numbers.  I don’t think it’s non-intuitive, just different from Excel.

So I put together a couple quick screencasts of making a line graph and a scatter plot, in the hope it would give a starting point for people new to Numbers.  (Flash movies, 7.8 and 5 MB respectively.  No sound, because my kids are still asleep.)  I’ve never tried this before, but hopefully they’ll work.

A truly amazing paper in today’s Nature¹ shows 2-photon microscopy videos of T cells entering the brain in search of their target antigen.  The title of this post is taken from the commentary,² also in Nature.

Disease-causing T cells first adhere to the inner walls of the pial vessels and then crawl in continuous contact with activated endothelial cells, most often in the opposite direction to the blood flow. …  After crossing the blood-vessel wall, the lymphocytes move along the outer surface of the vessel, encountering an array of antigens displayed by antigen-presenting cells, including macrophages. …  Last, the cells detach from the outer surface of the blood vessel and enter the spinal cord, travelling most often alongside penetrating vessels. In the spinal cord, they initiate tissue injury.²

There are a myriad of stunning videos and images.  Here’s just one video of the many, showing T cells (in green) exiting a blood vessel in the brain, and (in part 1) swimming off into the brain tissue to spread devastation and destruction (since these are autoimmune, self-reactive T cells):

1. Bartholomäus, I., Kawakami, N., Odoardi, F., Schläger, C., Miljkovic, D., Ellwart, J., Klinkert, W., Flügel-Koch, C., Issekutz, T., Wekerle, H., & Flügel, A. (2009). Effector T cell interactions with meningeal vascular structures in nascent autoimmune CNS lesions Nature, 462 (7269), 94-98 DOI: 10.1038/nature08478[↩][↩]
2. Ransohoff, R. (2009). Immunology: In the beginning Nature, 462 (7269), 41-42 DOI: 10.1038/462041a[↩][↩]

TRegs infiltrate into a tumor

One of the reasons the immune system doesn’t destroy tumors is the presence of regulatory T cells (TRegs) that actively shut down the anti-tumor response.  For once, there’s a little bit of encouraging news on that front.

TRegs are normal parts of the immune system.  They actively prevent other T cells (and so on) from attacking their target. ¹  What’s more, TRegs are antigen-specific.  That is, they recognize a specific target, just as do other T cells, but instead of responding by, say, destroying the cells (like  cytotoxic T lymphocyte) or by releasing interferon (like a T helper cell) a TReg’s response to antigen is to prevent other T cells from doing anything in response to that antigen.  In other words, TRegs cause an antigen-specific inhibition of the conventional immune response. ²

Back to tumors.  We know that immune responses don’t routinely eliminate tumors by the time they’re detectable.  There is some evidence that lots of very small, proto-tumors, are in fact destroyed by the immune system very early on, before they’re clinically detectable, but those tumors that survive that attack seem to be pretty resistant to immune control.  At least part of that resistance is because TRegs get co-opted into the tumor’s control (see here, and references therein, for more on that).

So if TRegs are antigen-specific, and TRegs control immune responses to the tumor, what are the tumor antigens that are driving the TRegs?

I would have assumed that TRegs are looking at many, many tumor antigens, including both normal self antigens³ as well as classical tumor antigens.⁴  But a recent paper⁵ suggests, to my surprise, that this assumption is wrong.  Instead, “Tregs in tumor patients were highly specific for a distinct set of only a few tumor antigens“. ⁵ What’s more, eliminating TRegs cranked up the functional immune response, but only to those antigens TRegs recognized — as you’d expect, if the suppression is indeed antigen specific.

This is interesting for several reasons.  If TRegs can be specific for tumor antigens, then at least in theory ((In practice, we don’t quite have the tools yet, I think) it should be possible to turn off these TRegs while leaving the bulk of TRegs intact (and therefore not precipitating violent autoimmunity).  It also suggests that if the TRegs are only suppressing a subset of effector T cells, there’s something else preventing most effector T cells from, well, effecting.  Maybe those are antigen non-specific TRegs, or maybe there’s something else we need to know about.

I’d like to see this sort of study replicated, and a little more fine-tuning on identifying the TReg’s targets (the readout was intentionally fairly coarse here, in order to identify as many as possible).  Still, it’s an unexpected, and potentially very useful, observation.

1. It’s still not quite clear how they do this[↩]
2. There are also antigen-nonspecific TRegs, but we will ignore them for now.  They’re not as effective as the antigen-specific sort, anyway.[↩]
3. Because TRegs, unlike most immune cells, can be stimulated by normal self antigens[↩]
4. That is, antigens that are mutated, or dysregulated, and that therefore act as standard targets for immune cells[↩]
5. Bonertz, A., Weitz, J., Pietsch, D., Rahbari, N., Schlude, C., Ge, Y., Juenger, S., Vlodavsky, I., Khazaie, K., Jaeger, D., Reissfelder, C., Antolovic, D., Aigner, M., Koch, M., & Beckhove, P. (2009). Antigen-specific Tregs control T cell responses against a limited repertoire of tumor antigens in patients with colorectal carcinoma Journal of Clinical Investigation DOI: 10.1172/JCI39608[↩][↩]