Check Up: So far, very little flu

CDC Director Gerberding Gives Green Light to G...
CDC Director Gerberding Gives Green Light to Gardasil then Goes to Work for Merck (g1a2d0049c1) (Photo credit: watchingfrogsboil)

Check Up: So far, very little flu

The U.S. Centers for Disease Control and Prevention has confirmed what you already guessed: This has been a remarkably mild flu season.
The influenza virus likes cold weather, so infections normally occur from October through March. But technically, the flu season doesn’t start until labs that test respiratory swabs from sick people find the virus in more than 10 percent of the samples.
This season, that threshold wasn’t reached until the week ended Feb. 11, making this the kindest flu spell in 29 years.
Pennsylvania, for example, had only 80 confirmed cases in all of January – barely more than one achy, feverish, nauseated citizen per county.
What’s going on?
No one really knows.
“With flu, everything is unpredictable,” said immunologist Scott Hensley, a flu expert at the Wistar Institute in Philadelphia. “I don’t think we’re out of the woods; it could just be a delayed season.”
Then again, maybe the flu has been as scarce as snow because snow has been scarce.
“Flu is more easily transmitted in colder temperatures. This has been a mild winter,” Hensley said.
Another theory is that the population has high levels of immunity to the influenza strains now circulating, which include the one that caused the 2009 “swine flu” pandemic. Because the strains have been so stable, people have had time to develop antibodies against them. Vaccination has also boosted immunity.
Although Hensley subscribes to this theory, he adds a caveat: “If that’s true . . . the virus will start mutating” to evade human defenses. “A novel strain might emerge in the next couple of months.”
While there’s no room for complacency, let us celebrate the signs, monitored by the CDC, that the flu has given the nation a respite:
One person per 100,000 has been hospitalized with the flu since October. That’s a 95 percent drop from last season’s rate of 22 people per 100,000.
Only 1 percent to 2 percent of visits to doctors since October have been for flulike illness. The usual rate is 3 percent to 8 percent.
This flu season, there have been three flu-related deaths among children, compared with 122 pediatric deaths last season – and 348 during the 2009 pandemic.
– Marie McCullough

Read more: Watch sports videos you won’t find anywhere else

Who defends lilly and novonordisk diabetes meds

Source: Pharmalot

conflictsofinterest212The scene at the European Association for the Study of Diabetes being held in Lisbon this week has included a heated debate over the extent to which a particular type of diabetes medicine called GLP-1 therapies can increase the risk of pancreatic and thyroid cancer. These include Byetta, which is sold by Eli Lilly and its partner, Amylin Pharmaceuticals, and Victoza, which is marketed by Novo Nordisk.
The issue has actually been percolating for more than a year (read here), although a review published two months ago in Gastroenterology that reviewed the FDA database of side effects showed patients taking Byetta had a much larger chance of developing pancreatitis, which can increase the risk of tumors (read the abstract). The drugmakers maintain their meds are safe.
And so there was a debate among researchers in attendance, including Peter Butler of the University of California at Los Angeles, who was one of the authors of the Gasterenterology study. One of his opponents was Michael Nauck, head of the Diabeteszentrum Bad Lauterberg in Germany, who told Bloomberg News that “the bulk of findings tends to speak against such an association. There is no general agreement.”
In fact, he believes the FDA database not only fails to establish a link to thyroid and pancreatic cancer, but may instead show the drugs could protect against other forms of cancer, such as prostate tumors. “Looking at same database and using very, very similar methods, I find evidence that some forms of cancer are reduced.”
Similarly, Matteo Monami, a physician at the University of Florence and Carreggi Teaching Hospital in Italy, told Bloomberg that the Gasteroenterology study is “erroneous analysis” and its results “are really not reliable at all.” He presented a study showing no increase in cancer or pancreatitis for so-called dipeptidyl peptidase-4 inhibitors such as Januvia.
However, what was not made clear is that both Nauck and Monami have ties to the drugmakers that sell the GLP-1 treatments. For instance, Nauck has worked as a consultant to both Lilly and Novo Nordisk, and also received clinical research grants from both drugmakers (look here). And Monami has received speaking fees from Lilly (see this).
Of course, it does not automatically follow that one or both researchers is biased due to their relationships with either drugmaker. Just the same, such ties would have been helpful to know, given that they were rather outspoken in defending the medications at an important meeting where the scientific community gathers to absorb and ponder research that is used to influence medical practice.

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Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial

Age-standardised disability-adjusted life year...                          Image via WikipediaGriffin SJ, Borch-Johnsen K, Davies MJ, et al. Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trialLancet. 2011 Jun 24. (Original) PMID: 21705063

BACKGROUND: Intensive treatment of multiple cardiovascular risk factors can halve mortality among people with established type 2 diabetes. We investigated the effect of early multifactorial treatment after diagnosis by screening.
METHODS: In a pragmatic, cluster-randomised, parallel-group trial done in Denmark, the Netherlands, and the UK, 343 general practices were randomly assigned screening of registered patients aged 40-69 years without known diabetes followed by routine care of diabetes or screening followed by intensive treatment of multiple risk factors. The primary endpoint was first cardiovascular event, including cardiovascular mortality and morbidity, revascularisation, and non-traumatic amputation within 5 years. Patients and staff assessing outcomes were unaware of the practice`s study group assignment. Analysis was done by intention to treat. This study is registered with, number NCT00237549.
FINDINGS: Primary endpoint data were available for 3055 (99.9%) of 3057 screen-detected patients. The mean age was 60.3 (SD 6.9) years and the mean duration of follow-up was 5.3 (SD 1.6) years. Improvements in cardiovascular risk factors (HbA(1c) and cholesterol concentrations and blood pressure) were slightly but significantly better in the intensive treatment group. The incidence of first cardiovascular event was 7.2% (13.5 per 1000 person-years) in the intensive treatment group and 8.5% (15.9 per 1000 person-years) in the routine care group (hazard ratio 0.83, 95% CI 0.65-1.05), and of all-cause mortality 6.2% (11.6 per 1000 person-years) and 6.7% (12.5 per 1000 person-years; 0.91, 0.69-1.21), respectively.
INTERPRETATION: An intervention to promote early intensive management of patients with type 2 diabetes was associated with a small, non-significant reduction in the incidence of cardiovascular events and death.
FUNDING: National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Wellcome Trust, UK Medical Research Council, UK NIHR Health Technology Assessment Programme, UK National Health Service R&D, UK National Institute for Health Research, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Novo Nordisk, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Merck.

Statins, Diabetes, and Attacking a Meta-Analysis

P-values from Fisher's meta analysis applied t...Image via Wikipedia

Source: Evidence Based Medicine

I’m a little late reading the June 19th 2010 Lancet, but was intrigued to find letters in response to the meta-analysis by Sattar et al. looking at whether statin therapy increases the risk of diabetes.

I had previously written about this well-performed meta-analysis, and also written about some unfair ways that people use to try to attack randomized trials, and these letters provide an interesting (at least to me) intersection between these posts.
Letters in academic scientific journals are sociologically revealing. There’s typically a polite veneer on even the most vicious attacks. Letters written to European medical journals have a somewhat different feel from those to American medical journals, and letters to the Lancet often seem to have a sneering tone that would be unusual to find in the NEJM or JAMA.
One letter about the meta-analysis objects that the results cease to be statistically significant when diabetes diagnosed only by physician report are excluded, and secondly that the results involved a post-hoc analysis of the data, with the warning that we might fall victim to the logical fallacy, “Post hoc ergo propter hoc“.
Are these fair objections?
Diagnosing diabetes by physician report rather than blood glucose measurement is likely to lead to misclassification: some patients will be classified as having diabetes who don’t, and some who have diabetes will be missed. In an RCT, though, misclassification like this will almost certainly be random as well, leading to random misclassification bias. Bias of this sort is toward the null hypothesis (no difference between the groups), as you can convince yourself of if you imagine that the classification is perfectly random such that there is no relation between the classification and diabetes. Under such perfect misclassification, the two groups would have equal numbers of patients classified as having diabetes and there would be no difference between treatment and control. In a meta-analysis that found higher rates of diabetes in patients receiving statins, misclassification bias can be expected to have somewhat reduced the true effect, not to have created an effect out of thin air.
The second objection might be called the “post-hoc-ergo-propter-hoc-fallacy fallacy”. The actual fallacy is, of course, a way of saying that just because B follows A, you should not conclude that A caused B. This question of causality is central to epidemiologic research and one of the primary reasons for performing randomized trials, which have particular strengths when arguing for causality. The fallacy has nothing to do with performing post hoc analyses of trials. (To be fair, it’s possible the letter writer understood this and was being humorous when writing of this fallacy.) The main problem with a post hoc analysis of a randomized trial is that it often involves multiple comparisons/data dredging, where statistical blips are likely to confuse the issue of what is a true effect. As discussed in my earlier post, a prime issue preceding this meta-analysis was whether JUPITER had found just such a random blip or detected a real problem. The meta-analysis’ reason for being performed was primarily to answer this question, and in such a setting there is nothing at all concerning about going back to previously conducted RCTs and performing post hoc analyses looking for diabetes effects. No data dredging was involved, and the analysis should not be looked at askance simply for being post hoc. Revealingly, the meta-analysis found an increased risk of diabetes even when data from JUPITER were excluded.
A second letter complained that the analysis would have been better had it been carried out using hazard ratios rather than odds ratios. While this would likely be true, such an analysis was not possible given the information available to the authors, and it is hard to imagine why an OR analysis would have shown statins to be causing diabetes if it were not true. The same letter also re-raised the possibility that statins appeared to be causing people to have more diabetes by keeping them alive longer to develop diabetes. However, the authors had already addressed this in their meta-analysis and reiterated in their response to the letters that differences in survival were much too small to produce such an effect.
A third letter mis-states the definition of a type I error on its way to arguing that the meta-analysis should have used 99% confidence intervals (p-value cutoff of 0.01) for some reason that was not made terribly clear, but seemed related to concerns that a very large meta-analysis would be more likely to detect a spurious result. It is true that given the enormous N in the analysis, it was possible to find a statistically significant difference in diabetes rates that is likely of little clinical significance, but this has nothing to do with the truth or falsehood of the result itself. The letter also argues that the result is biologically implausible, though it does not seem implausible that a medication could increase diabetes rates during the time of a randomized trial, if only by raising blood sugars in patients near the margin between insulin resistance and diabetes.
A fourth letter suggests that the “diabetes” found in the study might be different in terms of patient-important outcomes than the clinical condition we think of as diabetes. That is, statins might be raising blood sugars in a way that is harmless. While this is possible, it’s interesting that when a drug class raises blood sugar people are willing to argue it might be harmless, but when a drug class lowers  blood sugar there’s a tendency (at least for the manufacturer) to argue that blood sugar control is an excellent surrogate for clinical outcomes. The author of the letter suggests an analysis that might have been done to sort out this issue, which the authors of the meta-analysis correctly point out would not have answered the question.
There were a few other replies to the article, which I have not detailed. Overall, though, this is a fairly typical picture of what happens when someone publishes a trial that conflicts with conventional beliefs, such as “statins are good”. This occurs even when the conflict is quite minor — the meta-analysis merely shows a small increase in diabetes that would be heavily outweighed by cardiovascular benefit in anyone who would be appropriately treated with a statin.
There is no guarantee that the meta-analysis by Sattar et al. is correct about statins and diabetes, but none of the letters published by Lancet raise a sensible reason to think that the post-analysis state of knowledge should change: it is now far more likely than not that statins cause a small increase in diabetes risk. Our response to a meta-analysis like this should be to congratulate the authors on a job well done, while recognizing the possibilities for errors and chance to disrupt the conclusions. It should not be to search high and low for far-fetched flaws that would allow us to discard the inconvenient likelihood that a new statin side-effect has been detected.

Mortalidad en pacientes con diabetes

The blue circle symbol used to represent diabetes.Image via Wikipedia

The Emerging Risk Factors Collaboration. Diabetes Mellitus, Fasting Glucose, and Risk of Cause-Specific Death. N Engl J Med 2011; 364: 829-841.   TC (s)   PDF (s)


Se conoce desde hace tiempo que la presencia de una diabetes mellitus aumenta el riesgo de sufrir (y morir por) una enfermedad cardiovascular. En los últimos años también se van publicando trabajos en los que se asocia con un exceso de mortalidad por otras causas.


Estudiar la asociación de la diabetes mellitus y de la hiperglicemia con la mortalidad específica por determinadas causas y estimar el efecto de la misma sobre la esperanza de vida.

Perfil del estudio

Tipo de estudio: Metaanálisis
Área del estudio: Pronóstico
Ámbito del estudio: Comunitario


Se incluyeron en el análisis los datos individuales de los participantes en 97 estudios prospectivos en los que no se seleccionó a los participantes en función de la presencia de enfermedades crónicas y en los que se disponía de la información al inicio sobre si los individuos presentaban una diabetes y de sus cifras de glucemia y sobre la causa de la muerte y cuya duración excedía un año. Se dispuso de la información inicial de todos los participantes sobre su edad, sexo, hábito tabáquico e IMC. Se dispuso de información sobre otros factores de riesgo de una parte importante de los individuos.


Se incluyeron en el análisis los datos de más de 820.000 participantes que suponían un seguimiento total de más de 12 millones de personas-año con 123.000 muertes. La edad media inicial fue de 55 años y un 52% eran varones. Casi un 60% eran europeos. Un 6% presentaban una diabetes al inicio del estudio.
Las tasas crudas de mortalidad fueron superiores en varones que en mujeres y en diabéticos que en no diabéticos y para los tres grupos de causas (vascular, cáncer y otras) [fig.1].

Figura 1. Tasas crudas de mortalidad  (por 1.000 personas-año).

Este exceso de mortalidad se mantuvo en el análisis ajustado por edad, sexo, tabaquismo e IMC (tabla 1). También se detectó un exceso de mortalidad en función de la glicemia en las personas con valores iniciales por encima de 100 mg/dL. Los excesos de riesgo detectados casi no cambiaron cuando se introdujeron otros factores de riesgo en el análisis. El exceso de mortalidad observado fue superior en las personas más jóvenes y en las mujeres. El exceso de muerte observado fue inferior en los estudios más recientes.

Tabla 1Hazard ratio (HR) de muerte por diferentes causas en pacientes diabéticos y no diabéticos con glicemias >100 mg/dL (por cada mmol/L de exceso de  glicemia).
Diabetes mellitus Glucemia basal >100 mg/dL
(por cada mmol/L)
Mortalidad total 1,80 (1,71 a 1,90) 1,10 (1,09 a 1,11)
Cardiovascular 2,32 (2,11 a 2,56) 1,13 (1,11 a 1,15)
Cáncer 1,25 (1,19 a 1,31) 1,05 (1,03 a 1,06)
Otras causas 1,73 (1,62 a 1,85) 1,10 (1,07 a 1,12)

Las localizaciones de los tumores y las enfermedades no cardiovasculares ni tumorales para las que se detectó un exceso de mortalidad en diabéticos se recogen en la tabla 2.

Tabla 2 Localizaciones tumorales y otras enfermedades no cardiovasculares para las que se detectó un exceso de mortalidad en diabéticos.
Tumores Otras enfermedades
Otras infecciones
Digestivas no hepáticas
Causas externas

Se estimó que una persona de edad media con diabetes sin antecedentes de enfermedades cardiovasculares tenía una esperanza de vida unos 6 años inferior a una persona sin diabetes de la misma edad. Los años de vida potencial perdidos eran superiores en mujeres que en varones y tanto más altos cuanto más joven era el paciente (fig. 2).

Figura 2. Diferencia en la esperanza de vida en pacientes  diabéticos y no diabéticos en función de la edad y el sexo.

La mayor parte de esta diferencia se debía a las enfermedades cardiovasculares, seguida de las otras enfermedades y el cáncer.

Figura 3. Proporción de pérdida de años de vida potenciales por diferentes causas.


Los autores concluyen que los pacientes con diabetes presentan un mayor riesgo de muerte no sólo por enfermedades cardiovasculares, sino también por determinados tumores, enfermedades infecciosas, muertes violentas y enfermedades degenerativas.

Conflictos de interés

Financiado por becas de varias fundaciones públicas y por Pfizer


Este estudio es probablemente el llevado a cabo sobre una población más amplia entre los que estudian la relación entre la diabetes y la mortalidad por diferentes causas, por lo que sus conclusiones tienen un peso muy elevado. A la hora de valorar sus resultados hay que tomar en consideración sin embargo algunos aspectos: se trata de un metaanálisis de estudios observacionales, por lo que no se dispone de información sobre importantes posibles factores de confusión para todos los individuos incluidos. Sin embargo, en los análisis parciales en los que se controlaba el análisis por estos factores, las cifras encontradas variaban muy poco, por lo que es poco probable que disponer de esta información hubiese alterado de forma importante los resultados.
También hay que ser cauto a la hora de interpretar los resultados. Aunque las asociaciones encontradas es muy probable que sean reales, no se puede concluir que se trate de relaciones de causa-efecto y también es imposible deducir a partir de estos datos si aumentaba la incidencia de estas enfermedades o su letalidad.
Finalmente, no todos los diabéticos tienen el mismo riesgo de morir. En los estudios publicados recientemente se han encontrado asociaciones con una mayor mortalidad de los pacientes con diabetes con peor control metabólico, que sufrían más hipoglicemias graves, con menor actividad física, tabaquismo, que consumían menos fibra o que tenían menor tendencia a solicitar ayuda y a relacionarse con los demás.


  1. Bonds DE, Miller ME, Bergenstal RM, Buse JB, Byington RP, Cutler JA et al. The association between symptomatic, severe hypoglycaemia and mortality in type 2 diabetes: retrospective epidemiological analysis of the ACCORD study. BMJ 2010; 340: 4909  R   TC   PDF
  2. He M, van Dam RM, Rimm E, Hu FB, Qi L. Whole-Grain, Cereal Fiber, Bran, and Germ Intake and the Risks of All-Cause and Cardiovascular Disease–Specific Mortality Among Women With Type 2 Diabetes Mellitus. Circulation 2010; 121: 2162-2168.  R   TC (s)   PDF (s)  RC
  3. Nelson KM, Boyko EJ, Koepsell T. All-Cause Mortality Risk Among a National Sample of Individuals With Diabetes. Diabetes Care 2010; 33: 2360-2364.  R   TC (s)   PDF (s)
  4. Ciechanowski P, Russo J, Katon WJ, Lin EH, Ludman E, Heckbert S et al. Relationship styles and mortality in patients with diabetes. Diabetes Care 2010; 33: 539-544.  R   TC   PDF


Manuel Iglesias Rodal. Correo electrónico:

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