Low Carb Charlie

From Morbidly Obese to Wow!

Low Carb Charlie - From Morbidly Obese to Wow!

Once a Month Low-Carb Cooking?

For me, there’s always been several Faustian trade-offs between dining out versus dining in: while dining out is more convenient, it’s usually more expensive (though not always) and you consume a lot of other cheap, questionable, hard-to-pronounce ingredients (owing to oddities of industrial mass food production demands) that you didn’t ask for.  Many of these ingredients also aren’t regulated all that well, too, so who knows what the long-term implications could be to one’s health.  On the other hand, with home-cooked meals, you have better control over what type and the quality of ingredients you consume, but it also costs time and effort, which is becoming a scarce commodity.  However, the less difficult-to-pronounce ingredients one’s foods has–a.k.a. the less processed it is–the better it probably is for one’s health in the long-term.

I used to not view food in this way, which is probably why it was so easy to rationalize ordering off the fast food dollar menus en route to becoming 350+ pounds at one point in my life.  But I literally can’t go back to that mindset.

So how do I balance real food nutrition with the constraints of cost and convenience?

One answer came from a few blogs I read about slow cooker freezer recipes.  The idea is that one buys food in bulk or when it’s on sale, prepares some or all of the ingredients in a freezer bag or container, thaws a bag the night before, and cooks on low for 8+ hours in the slow cooker.  The concept is informally known as “once a month cooking.”  It was mostly some family’s mom that seriously used this method of food preparation on different blogs and YouTube videos I’ve seen, which makes sense.  Although I’m not a mother and don’t face nearly the same constraints, I would love to adapt the simplicity, cost, and convenience aspects and figured I would experiment soon and adapt this to low-carb cooking.

Freezer

Currently, I usually take a few hours during a weekend to prepare meals for the upcoming week in the fridge, but if I could invest the same amount of time to prepare even more meals, then that would help free up a few more weekend afternoons for me.

I’ve seen low-fat, Paleo, and gluten-free incarnations of slow-cooker freezer recipes but not any particular website or cookbook that I know of at least that focused exclusively on low-carb preparation in this particular way.

Hamilton Beach Slow Cooker

So here’s part 1 of some recipes I’ve modified that I could probably adapt to this method of freezer bag/container preparation–>thaw night before–>cook in slow-cooker for 8+ hours.  Usually each recipe makes two one-gallon freezer bags.  So in order to do this type of once a month low-carb cooking, I would need 15 bulk recipes to freeze and store 30 one-gallon meals for a month.  Luckily, I have an extra freezer in the garage!

1. Spaghetti: <4g net per serving

2 X 2lbs. 85% lean ground beef (preferably grass-fed when possible)

2 X 1 lb. Italian sausage (I like Purnell’s @ Kroger due to no MSG in ingredients)

2 X 1 TBSP minced garlic

Seasonings

2 X 1 16oz. Jar of Rao’s Arrabiata Sauce (18g net carb for whole jar @ Walmart)

2 X 2 7oz. bags of Spinach Angel Hair Miracle Noodles (these store well in the fridge for some time)

On two burners simultaneously, while mixing in the garlic and other seasonings, I would spend about 15-20 minutes cooking the meat mixture only until it was no longer pink.  Then I would drain the mixture, let cool for about 10 minutes or so, and finally into two evenly distributed one-gallon freezer bags.  Label it “spaghetti” and place into the freezer, squeezing as much air out as possible and laying as flat as possible.

The night before I want to have this spaghetti, I would set from the freezer into the fridge to allow it to thaw.  Then the following morning I would dump into the slow cooker with one jar of Rao’s sauce and 2 bags of angel hair miracle noodles (drained and cut into smaller strands), then set for 8-10 hours on low.

The only carbs should really come from the sauce and a tiny bit from the minced garlic, about 19g net carbs for the entire pot, but makes about 5 servings (so less than 4g net per serving).

2. Chicken and Broccoli Alfredo: ~6-8g net carbs per serving

Similar in concept to the spaghetti….

2 X 2 lbs of boneless skinless chicken breast, diced (I usually can call my local butcher ahead of time to dice it for me at no extra charge–a real time saver!)

2 X 1 160z. bags of frozen broccoli florets

2 X 1 16 oz. jars of light alfredo sauce (~2g net per 1/2 cup serving is pretty good)

Seasonings

2 X 2 7oz. bags of Spinach Angel Hair Miracle Noodles (these store well in the fridge for some time)

Unlike the spaghetti, I’m not going to cook the chicken ahead of time.  I’m simply going to mix the seasonings with the diced chicken breasts, place 2 pound’s worth in each one-gallon freezer bag (again, 2 bags total), along with one pound of frozen broccoli florets.  Label, squeeze the air out, and freeze.

After thawing the day before in the fridge, I simply dump one bag’s content into the slow cooker with one jar of alfredo sauce and 2 bags of the angel hair miracle noodles (drained and cut).  Set on low for 8-10 hours.

The main sources of carbs in this dish would come from the broccoli and then the alfredo sauce.

3. Chicken Adobo: ~0-1g net carb per serving

2 X 2.5-3 pounds of chicken wings

2 X 1/2 cup of apple cider vinegar

2 X 1/2 soy sauce

2 X 1 cup water

2 X 3 bay leaves

2 X 1 TBSP of minced garlic

Seasonings

A Filipino classic done easier.   For each one-gallon bag (2 total), put 2.5 to 3 pounds of chicken wings with 1/2 cup apple cider vinegar, 1/2 cup soy sauce, 1 TBSP of minced garlic, 1 cup water, and seasonings.  Seal tightly and stick in the freezer.

Same routine as above in terms of thawing in the fridge the day before.  And on the day of, just add 3 bay leaves to the slow cooker along with the freezer bag contents, then set on low for 8-10 hours.

The only real source of carbs would be a miniscule amount found in the minced garlic.

Instead of white or brown rice, I might use the food processor to make some cauliflower rice the day of, or more likely, ahead of time.  See this recipe for details.

4. Texas Chili: <1g net carbs per serving

2 X 3lbs ground chuck

2 X 1/4 cup chili powder

2 X 1 TBSP red pepper flakes

2 X 1 TBSP oregano

2 X 1 TSP cumin

2 X 1 TSP salt

2 X 4 oz cream cheese

2 X 1 TSP pepper

2 X 2 cups of water (1 cup = 8oz)

On two burners simultaneously, cook ground beef with dry seasonings until just no longer pink.  On the day of cooking in the slow cooker, pour in the water and cream cheese, then set on low for 8-10 hours.

The only real source of carbs would come from the 4 ounces of cream cheese (about 4g net) and makes about 6 hearty servings.

5. Pork Chops: ~2g net carbs per serving

2 X 1 family pack of pork chops (about 10 each)

2 X 1 can of cream of mushroom soup

2 X 1/2 cup of low-sugar ketchup

Salt

Pepper

Mix the soup and ketchup together, then add the salt and peppered the pork chops, throw in a freezer bag.  Then dump in slow cooker on low for 8-10 hours.

Each pork chop would be about 2g net carbs.

6. Beef Stew: 2g net carbs per serving

2 X 2.5 lbs beef stew

2 X 0.5 lbs bacon (just oven bake for 20 minutes at 400F, then dice into smaller pieces)

2 X 1 chopped green pepper (Kroger)

2 X 1 TBSP minced garlic

2 X 1 TBSP Worcestershire Sauce

2 X 3 TSP salt

2 X 1 TSP black pepper

2 X 1 TSP garlic powder

2 X 1 TSP onion powder

2 X 1 TSP oregano

2 X 3 cups of beef broth

Mix all ingredients into one-gallon freezer bags, squeeze out excess air, and throw in freezer.  After thawing for a day, just set slow cooker on low for 8-10 hours.

Only real sources of carbs would be in the green peppers and then the beef broth.  I’d say this makes about 5 – 6 hearty servings for about 2g net per serving.

7. Chicken Parmesan: 4.5g net carbs per chicken breast serving

2 X 6-7 boneless skinless chicken breasts

2 X 1/2 cup almond flour

2 X 1/4 cup grated Parmesan cheese

2 X 2 eggs, beaten

2 X 1 TBSP salt and pepper

2 X 1 16 oz. jar of spaghetti sauce

2 X 1 TBSP olive oil

Shredded Parmesan cheese or mozarella

Create an egg bath for the chicken in one bowl.  Mix flour, salt, pepper, and grated Parmesan cheese together in another bowl.  Coat the chicken in the egg bath and then coat in the dry ingredients. Then place coated chicken breasts into freezer bags. Dump the rest of the dry ingredients into the bag. Squeeze out excess air and store in freezer.

On the day of slow cooking, pour the TBSP of olive oil to lightly, evenly coat bottom of ceramic insert bowl, then carefully place chicken on top of the oil, then the cheese, and finally the jar of spaghetti sauce. Cook on low for 8-10 hours.

The sources of carbs would be some in the almond flour (depending on brand, assuming Bob’s Red Mill here) and then the spaghetti sauce.  About 4 – 5g net carbs per chicken breast.

8. Curry Pork Roast: ~2g net per serving

2 X 3 lbs pork loin roast

2 X 2 TSP red or yellow curry powder

2 X 2 TSP salt and pepper

2 X 1 TSP cayenne pepper

2 X 1 TBSP minced garlic

2 X 1.5 cups of water

2 X 3 TBSP balsamic vinegar

2 X 3 TBSP low-carb ketchup

2 X 1 TBSP red pepper flakes

Evenly coat dry ingredients (except red pepper flakes) and garlic into the roast.

Spray a large nonstick skillet with nonstick cooking spray and set over medium-high heat. When skillet is hot, sear roast, turning every 30 to 60 seconds to lightly brown all sides.  Allow to cool and set in freezer bag.

On day of cooking, add water to ceramic insert bowl and set to low for 8-10 hours.  Make sure internal temperature is about 145F.

To make the sauce that lightly covers the roast, mix red pepper flakes, ketchup, and balsamic vinegar together.

Main sources of carbs would be the sauce mixture and a tiny bit from the garlic.  Should make six hearty servings, or about 2g net per serving.

I’ll try to come up with 7 more recipes next time so I can fully do the 30 day experiment.

Low-Carb and GMO Salmon

With a Paleo/Primal twist to low-carb dieting, I feel like I’ve largely sidestepped the GMO vs. non-GMO debate so far as a consumer because the majority of GMO products and ingredients show up directly in processed products and starchier vegetables foods I don’t normally purchase or eat.  Although without any mandatory labeling requirements (and I actually do pay close attention to nutrition labels), it’s difficult to know for sure to what degree I’ve managed to successfully avoid GMO ingredients altogether.

GMOs in US

However, I learned recently that the FDA is set to approve genetically engineered salmon soon and that most likely it will not be labeled as such.  This new AquAdvantage salmon is “a breed of Atlantic salmon genetically modified to grow twice as fast as its natural counterparts.“  Approval would start an influx into the US market of other types of genetically modified meat currently in the R&D pipeline.

AquAdvantage Salmon

I would personally be reluctant to knowingly try genetically modified fish myself, and opt to take my chances on what evolution has already engineered in terms of genuine wild-caught salmon varieties.  Eat Wild has a comprehensive list of resources to obtain such catches from local sources.

Of course, the best bet is to do research on where, how, and who raises the fish or anything else one consumes.  Such knowledge effectively reduces one’s need for well regulated nutrition labels, which aren’t really that well regulated.

I used to think our food supply was tightly and independently regulated, but the more I read on this subject, the more skeptical I become.  The most recent book I read on this subject, Pandora’s Lunch Box, discusses at length regulatory loopholes and gaps with food additives.  It’s also a pretty entertaining read!

Our FDA (and USDA, and the other dozen or so agencies in which food safety is divvied among–read Food Politics for a good primer) effectively trusts those it oversees to self-regulate voluntarily, but food companies don’t see themselves as public health agencies.  Nor should consumers expect food companies to act like health agencies.  There’s also a long-standing revolving door between various food companies (including GMO firms) and regulatory agencies that such agencies are supposed to oversee.  Long story short: given the lack of robust, independent regulation of our food supply in general and food industry self-regulation, I wouldn’t, as a consumer, put too much faith in FDA approval meaning that genetically engineered salmon is safe for human health or the environment.

However, my biggest beef with GMOs has to do with labeling and transparency.  After all, there are many unregulated (and even harmful) food ingredients that people regularly consume, like hydrogenated vegetable oil (i.e. trans-fat), that people eat anyway.  Most consumers don’t even bother to read a label.  However, at least many of those questionable ingredients (again, Pandora’s Lunch Box is a good, recent read on this) are labeled so at least more conscientious consumers can legitimately make the decision of whether to avoid such products.

Just Label It

There would need to be mandatory labeling requirements if genetically modified salmon is approved for human consumption.  Despite overwhelming support for such a measure in California recently, corporate lobbying and campaigning won out and there is still no requirement to require such labeling.  In response to this and the FDA about to approve genetically modified salmon, Whole Foods and other grocers have voluntarily pledged to require such labeling in their stores but not until 2018.  I guess that’s better than nothing.

Without mandatory labeling requirements, it would be difficult as a consumer to make a genuine decision at a conventional grocery store or restaurant on whether to consume GMOs or not.  Independently of whether one is for or against GMOs, they should be clearly labeled as such.  That way, those who are for GMOs can purchase such products themselves and support these businesses, and those who are against or unsure can do the opposite.  Also, people nowadays seem to have increasingly more food allergies: if someone has, say, an allergy to fish but eats a tomato with a transgenic fish gene inserted, how would such people even know?  With the abundance of novel ingredients that flood the dinner plates every year, would a transgenic gene introduce more allergens?  How could that be known?

From a consumer interest standpoint, there’s no reason not to label GMOs.  In fact, mandatory GMO labeling enjoys broad support from consumers both those for and against on this issue.  At the very least, consumers can opt to be part of this mass experiment.  Or not.

Label It

What are your thoughts on GMO labeling in general?  And would you personally eat or order genetically modified salmon once FDA gives it the green light?

Tactics for Breaking Weight Loss Plateaus

Ivan Drago

Tomorrow I’ll be leading a discussion with my “Friends in Low-Carb Places” work support group on the topic of “tactics for breaking weight loss plateaus.”

Here’s my approach to this issue with three prefaces:

1.) In the continual journey to better health, it’s all about n=1 experimentation to see what works well for you and what doesn’t work well for you, meaning that one size may not fit all and that mileage may vary person to person even under similar approaches. I also read a fair amount of articles, books, blogs, and studies about nutrition and exercise, always trying to take away something I could potential experiment with at a later date (part of how I generate different hypothesis to try on myself).

2.) So if, after a few weeks of my scale weight stalling and not losing inches, I view this not as a personal failure or defeat but rather as valuable feedback and motivation to experiment or tweak some variable in my low-carb lifestyle.  Again, going back to Einstein’s definition of insanity, applied to weight loss.  The low-carb aspect (limiting to 20g or less net carbs per day) of my lifestyle change was the one constant throughout because not suffering through constant blood sugar swings was and is a positive.

3.) When I was losing weight (though not so much in maintenance mode nowadays), I kept a fairly detailed food and exercise diary so that when it came time to experiment I had a baseline to analyze against.  Like any good science experiment, especially on oneself, it definitely helps to keep some kind of record or journal.  Each tweak or experiment (and I would only add one variable at a time), I would run for about two weeks and re-assess my results.  Sometimes they didn’t work but most of the time they did.

Here are some of the variables I’ve tweaked:

  • Upping my fat intake while lowering my protein intake (to reduce incidence of gluconeogenesis, which is easy to do on a low-carb diet)
  • Cutting out gluten and wheat
  • Cutting out or limiting my use of vegetable seed oils (i.e. soybean, corn, canola, cottonseed), substituting use more saturated fats like butter and coconut oils, and sometimes olive oil
  • Cutting out or limiting my use of artificial sweeteners
  • Cutting out or limiting my use of sugar alcohol products (like Atkins bars, Detour bars, sugar-free candies, etc…)
  • Cutting out or limiting my use of caffeine (I found out that I was using caffeine to substitute for proper amounts of sleep)
  • Cutting out or limiting my use of dairy products
  • Increasing my green vegetable intake
  • Cutting out or limiting use of products that have MSG in their ingredients (I notice that my appetite gets more ravenous for some reason)
  • Cutting out diet soda completely (seems to make me crave sweeter things)
  • Dining in more frequently versus dining out
  • Try more grass-fed meats and organic vegetables
  • Supplements: Magnesium citrate or chelate, vitamin D3, probiotics, vitamin K2, CoQ10, L-Carnitine, Tonalin CLA, multivitamins (I’ve experimented with different combinations of these over time)
  • Sleep (trying to get 7 or 8 hours instead of 5 or less–sometimes with aid of melatonin)
  • Intermittent fasting (for the most part I still eat one large dinner per day)
  • Eating twice or three times a day versus once a day
  • Walking laps during work breaks or lunch
  • Spending more time outside in the sun
  • Full fasting (24-36 hours)–I occasionally did this once a week but only when I was sure I was in nutritional ketosis
  • Calorie cycling
  • Exercising in the morning vs. afternoon
  • Exercising once versus twice a day
  • Exercising 2 to 3 times a week versus 5 to 7 times (I noticed that exercising sometimes correlated with increased hunger)
  • Substituting resistance training for machines
  • Free weights
  • High Intensity Interval Training (HIIT)
  • Endurance cardio vs. sprinting
  • Racquetball versus treadmill or elliptical machine running

When you lose 150 pounds in 16 months like I did and maintain almost all of it for awhile afterward, plateaus are quite common and normal.  What matters is how you view and deal with it.

Also keep in mind, too, that as you lose weight and become healthier, your body changes and adapts as well.  What worked starting out may not work or work as well a year from now.

Finally, eating even less and/or exercising even more (on my old calories in calories out view) only goes so far.  I’ve learned to appreciate and respect the human body as a complex micro-ecosystem that evolved over millions of years instead of just as a simple calorie counter.  Even today as I’m in maintenance mode, I find myself constantly self-experimenting with different aspects of my lifestyle as I learn new things about nutrition and exercise.

Kick-Ass, No-Bake, Low-Carb Cheesecake!

 

Cheesecake 6   Cheesecake 7

Perhaps the biggest challenge in making low-carb cheesecakes is to find a creative substitute for the carb-heavy graham cracker crust.  So, I used chopped pecans with great results.  In fact, my wife demanded that I make more for her.  Best of all, it did not require the use of an oven and is so simple that even I could make it.  So here’s how I made it.

Ingredients and equipment:

1/2 Pound (or 8 oz) of Chopped Pecans (Fisher’s brand has 1g net carb per 1 oz)

1 8oz package of reduced fat Philadelphia Cream Cheese (<1g carb per 1 oz)

1 Cup Heavy Whipping Cream (0 carbs per TBSP)

2 TBSP Salted Butter (0 carbs per TBSP)

14 squirts of Liquid Sucralose (0 carbs per squirt)

1 9″ pie pan

1 Wire Whisk

1 Spatula (for mixing)

1 Measuring Cup

1 Mixing Bowl

1 Food Processor (like my Magic Bullet Blender) or Food Chopper

Cheesecake A

Pie Crust Instructions (10 minutes)

For the pie crust, I used a mixing bowl and spatula to mix the chopped 8 oz of Fischer’s chopped pecans (about 20-30% wasn’t processed into fine powder and left as whole pecans for different textures), 2 TBSP melted butter (0 carbs), and equivalent of 2 TSP of liquid sucralose.  I then poured the mixture into the pie pan and compacted the crust as evenly as I could all the way on the bottom, but ignored the side walls.  I then placed the pie crust in the freezer while I made my pie filling.

Cheesecake 2 Cheesecake 1

Pie Filing Instructions (5 minutes + 15 minutes to set)

For the filling, I mixed the slightly melted 8 oz of reduced fat cream cheese, 1 cup heavy whipping cream, and equivalent of 1/4 cup liquid sucralose (12 squirts).  I then poured the filling evenly over the pie crust that was set in the freezer and re-set it in the freezer for another 15 minutes before placing in the fridge until ready to chow down.

Cheesecake 3 Cheesecake 6

For the entire pie, it’s only 16g net carbs.

Nutrition Information

Cheescake B

Also, the ratios of this cheesecake make it fit well within a low-carb ketogenic diet that is low in carbs and protein but high in natural fats.  Bon apetit!

Final Flu Redux: Defining the Problem

Flu2

This is my last post for some time about flu vaccines, though in retrospect, perhaps this should’ve been my first.

If mass flu vaccines every year are the answer, then just how severe is the flu problem exactly, specifically as it relates to its worst possible outcome, death?  We know people die from the flu each year, but how is this data known, confirmed, and compiled to measure the mortality burden the flu imposes on society?

My initial thoughts before researching answers to these questions were as follows: since many illnesses can masquerade or mimic the flu virus, a lab-confirmed test should be performed to confirm a flu infection, and of those lab-confirmed flu infections, death certificates should state flu as the primary cause when tests corroborate a medical autopsy as such.  These statistics would then just be confirmed and consolidated from local and state levels into a national figure for a given year.  At least, that’s how I think an ideal way to aggregate this data would be more or less performed in order to accurately determine deaths attributed to the flu.

Post 2003, I’ve read anything from 23, 607 (Centers for Disease Control–CDC) to 36,000 (most quoted figure) and 41,400 (National Institute of Allergy and Infectious Diseases–NIAID) for the number of flu-caused deaths per year in the US.  But prior to 2003, the CDC used 20,000 as the annual figure of flu deaths.  However, the reason for this wide variance is simple: since most US death certificates don’t list the flu as a primary or secondary cause of death and of those that do, even fewer are lab-confirmed for the flu virus, the amount of deaths attributed to the flu each year must be guessed.  Different assumptions and even different computer models can yield vastly different estimates.

Also, a peculiar thing to note with the CDC’s annual mortality data from its National Vital Statistics Reports, is that influenza is grouped along with pneumonia, but the overwhelming number of deaths in this “Influenza and Pneumonia” category was from pneumonia instead of the flu (together, the 7th or 8th leading cause of death in the US, depending on year):

CDC Excerpt

The data above is from the 2006 final version of the NVSR.  The final versions of these mortality data reports seem to be produced on a 2- or 3- year lag and unlike preliminary versions, details the breakouts of the “Influenza and pneumonia” category.  Currently, 2009 is the latest version for finalized data, and going back 12 years to 1998, deaths attributed to influenza by age grouping and year are as follows:

CDC Table

So, grouping the flu with pneumonia (even though the two don’t always correlate) and using different modeling techniques and assumptions over time (instead of measuring more directly), it’s difficult to obtain an accurate, unbiased picture of how severe flu deaths are each year.  The CDC doesn’t agree with itself all the time either.  And I’m far from the only one to notice these discrepancies.

In 2006, the Journal of American Physicians and Surgeons voiced criticism of the CDC’s methodologies for estimating and classifying flu deaths:

The CDC and news media frequently proclaim that there are about 36,000 influenza-associated deaths annually.  Review of the mortality data from the CDC’s National Vital Statistics System(NVSS) reveals these estimates are grossly exaggerated.  The NVSS reports preliminary mortality statistics and distinguishes between influenza-related deaths and pneumonia-related mortality.  When the final report is issued, influenza mortalities are combined with the far more frequent pneumonia deaths, yielding an exaggerated representation of influenza deaths.  Pneumonia related mortality due to immunosuppression, AIDS, malnutrition, and a variety of other predisposing medical conditions is therefore combined with seasonal influenza deaths.  The actual influenza related deaths for the years 1997 to 2002 ranged from 257 to 1,765 annually.  These values are further overestimated by combining deaths from laboratory-confirmed influenza infections with cases lacking laboratory confirmation.

In the prior year, a review article titled “Are US flu death figures more PR than science?” was published in the British Medical Journal noting similar criticisms:

Meanwhile, according to the CDC’s National Center for Health Statistics (NCHS), “influenza and pneumonia” took 62 034 lives in 2001—61 777 of which were attributed to pneumonia and 257 to flu, and in only 18 cases was flu virus positively identified. Between 1979 and 2002, NCHS data show an average 1348 flu deaths per year (range 257 to 3006)…

William Thompson of the CDC’s National Immunization Program (NIP), and lead author of the CDC’s 2003 JAMA article, explained that “influenza-associated mortality” is “a statistical association between deaths and viral data available.” He said that an association does not imply an underlying cause of death: “Based on modelling, we think it’s associated. I don’t know that we would say that it’s the underlying cause of death.”…

Before 2003 CDC said that 20 000 influenza-associated deaths occurred each year. The new figure of 36 000 reported in the January 2003 JAMA paper is an estimate of influenza-associated mortality over the 1990s. Keiji Fukuda, a flu researcher and a co-author of the paper, has been quoted as offering two possible causes for this 80% increase: “One is that the number of people older than 65 is growing larger…The second possible reason is the type of virus that predominated in the 1990s [was more virulent].”…

If flu is in fact not a major cause of death, this public relations approach is surely exaggerated. Moreover, by arbitrarily linking flu with pneumonia, current data are statistically biased. Until corrected and until unbiased statistics are developed, the chances for sound discussion and public health policy are limited.

None of this is to be misconstrued as denying that people die from the flu or that the flu isn’t a serious issue, only that there are serious methodological issues in how this is defined, classified, and estimated on a large scale since it’s not measured in a direct, straightforward manner.

Also, taken in context with my previous post about how the CDC/FDA/HHS is more of a cheerleader and business partner (with Supreme Court blessing) with vaccine manufacturers than an independent regulator that places public health over private profits, like the media advertising of flu vaccine benefits (my first post about the flu), the magnitude of flu deaths has so far been greatly exaggerated to boost more vaccine sales through fear-mongering rather than cold hard facts and evidence that should guide public health policies.

More efforts should be invested in having better surveillance of flu-caused deaths instead of relying so much on assumptions and indirect guesses that are heavily sensitive to bias in either direction.  Lab tests should confirm flu virus infections and there should be more effort to have flu as a primary cause of death in instances where viral lab tests are positive and corroborate a medical professional’s judgment cause of death.  This would make deaths attributed to flu more reliable and easy to tally.  Also, the flu should be its own mortality classification separate from pneumonia.  All of these measures would provide us with a more accurate, consistent, and reliable picture of how big of a burden flu deaths are on society each year and hopefully guide public health policy decisions in a more rational manner.

Final Conclusions at this Time

Before concluding this off-topic series of posts about my own thoughts related to flu vaccines, I thought it’d be interesting to look at the CDC’s Vaccine Adverse Event Reporting Sytem (VAERS) database, more specifically using the query tool from the National Vaccine Information Center’s (NIVC) Medalert (it’s an easier, more intuitive interface).

I was just curious how many different adverse reactions are reported each year just for the flu vaccines for 1998-2009, and here is what I found:

VAERS

Considering that a typical flu season utilizes about 100 million vaccines, I thought it was pretty impressive that only 46,000 to 76,000 different adverse reactions are reported each year.  The 1986 National Childhood Vaccine Injury Act that was discussed in my second post about the flu, requires doctors and vaccine makers to report adverse reactions, but the NVIC estimates compliance with this requirement to be between 1% and 10% in any year.  Outside of it being part of the law, I’m unclear what the penalties, if any, there are for non-compliance.  But if we take the NVIC’s estimate seriously, that means the average number of adverse reaction reports where at least the flu vaccine is implicated over this 12 year period (64,437) is probably an under-estimation of the adverse reactions.

I also noticed that the 2001 CDC figure for flu deaths (257) didn’t seem to cohere well with the corresponding VAERS death records (432).  Given that the 257 is an indirect guess and VAERS records can be submitted by anyone (though mostly it seems to be some type of medical professional), it’s hard to make perfect sense of these data sets.

In conclusion,

1. The magnitude of flu-caused deaths in the US seems to be relatively small according to the CDC’s own data and greatly exaggerated (and inconsistently over-estimated and confusedly classified) as well;

2. The benefits of flu vaccines are also greatly exaggerated in the media compared to the results of independent, controlled studies; and

3. The number of adverse reactions in context of the number of flu vaccines given in an average flu season is also small by comparison.

How did such molehills become towering mountains?

Racquetball Tourney: 2nd Round Loss

Rball 2

Somehow I was ranked #2 in this cycle of the racquetball tournament and was able to survive the first round against Willie, but Mark was a real measuring stick to see how far I’ve come in my development and plus, it’s great feedback on what I need to do to improve. Mark, to his credit, has also greatly improved his game as well.

Flu Vaccine Redux: Vaccine Makers Immunized Against Product Liability?

Swine Flu

In my previous post about the flu vaccine, I demonstrated that clinical studies comparing vaccinated groups against similar non-vaccinated group showed very little absolute benefit (i.e. single digit improvement) which fluctuated by year.  This stands in stark contrast to the 50-60% effective rate advertised each flu season via various media outlets.

However, aside from gawking at some of the more questionable common ingredients in a typical flu vaccine shot (like formaldehyde), I didn’t do more research not only on the harms but also the mortality risk related to the flu.  So I did more research on both the harms and the magnitude of flu mortality in the US and came to some very interesting pieces of information, which I will share below by starting with what I found related to vaccine harms and then do a separate post on flu mortality statistics.

Flu Vaccine Manufacturers Immune from Tort Liability

In order to research the potential harm attributed to flu vaccine injections, I thought it would be interesting to see to what extent negligent manufacturers were sued by people injured by their vaccines, reasoning perhaps some other interesting facts would come to light along similar lines of the famed 1999-2004 Merck Vioxx fisaco.  So I simply did a Google search for “flu vaccine manufacturer lawsuits” and the first result was a 2009 NBC News article explaining that “Legal immunity set for swine flu vaccine makers.”  I vaguely remember 2009 as the year of the swine flu “pandemic,” but at the time, outside the media hype, I never realized everything that took place:

Vaccine makers and federal officials will be immune from lawsuits that result from any new swine flu vaccine, under a document signed by Secretary of Health and Human Services Kathleen Sebelius, government health officials said Friday.

Since the 1980s, the government has protected vaccine makers against lawsuits over the use of childhood vaccines. Instead, a federal court handles claims and decides who will be paid from a special fund.

My first thought was that it’s a little ironic that these particular manufacturers have lawsuit immunity from injuries that may result from their vaccines.  It also wasn’t clear that this move by HHS just applied to only swine flu vaccine manufacturers.  Finally, right, wrong, or indifferent, lawsuits are one traditional way patients can seek redress and accountability against negligent vaccine manufacturers.  This article led me to ask two further questions: 1. Is this still the case today with negligent vaccine manufacturers? and 2. What is this 1980s law briefly mentioned?

So, after finishing the NBC News article, I hit the back button and quickly came upon a 2011 Christian Science Monitor article summarizing that “Parents can’t sue drug firms when vaccines cause harm, Supreme Court says.”  This 2010 case wasn’t about flu vaccine harms per se but one for DTP instead:

The high court decision stems from the April 1992 administration of a vaccine to the Bruesewitzes’ then-infant daughter, Hannah. After being injected with the diphtheria-tetanus-pertussis (DTP) vaccine, Hannah suffered a series of seizures that left her developmentally disabled. Lawyers for the family say she will require medical care and supervision throughout her life.

The Bruesewitzes took their claim to the special Vaccine Court, but a month before they filed their petition, Hannah’s seizure disorder was dropped from a listing of injuries covered under the Vaccine Injury Act. The Vaccine Court ruled that the family would receive no compensation.

The family then filed suit in state court alleging that Wyeth Inc., the vaccine manufacturer, was negligent because it could have produced a safer version of the vaccine, but failed to do so. Lawyers for the family argued that the company should have upgraded its vaccine with a less dangerous version.

Wyeth lawyers countered that the federal Vaccine Injury Act preempts such claims made in state court.

I, of course, felt very sorry that the victim and her family, all of whom had to suffer for 18 years by that point just to be told they had no recourse in state court to hold a vaccine manufacturer accountable, but what about trying to seek some redress in this Vaccine Court mentioned? Further down the article further explained:

Millions of infant vaccines are safely administered each year throughout the United States. But government officials acknowledge that a small percentage of infants experience a severe negative reaction from a vaccine. In some cases the reaction can be fatal.

Faced with open-ended damages from lawsuits filed on behalf of those who suffer severe reactions from vaccines, drug manufacturers considered avoiding the vaccine market altogether.

In passing the National Childhood Vaccine Injury Act, Congress sought to strike a balance that would protect vaccine manufacturers from open-ended liability from private lawsuits while also creating a special fund to compensate those who suffer side effects from vaccines.

Roughly 100 to 200 claims for compensation are submitted each year to a special vaccine court. To date, the compensation fund has paid out $1.8 billion to 2,500 petitioners. The average award is about $750,000.

From here, I thought I’d see if any part of the pertinent Supreme Court decision explained this more and in plain English, so I just Googled it.

Here is the first paragraph of that decision:

SC Paragraph

If I’m reading the “no-fault compensation program” part correctly (I’m not a lawyer), it sounds like this vaccine court (under the HHS umbrella) can’t officially admit fault and won’t assign any blame to the vaccine makers they’re trying to shield but they’ll pay out injury claims, according to a prescribed vaccine injury table.

If one’s child or an adult had different vaccinations and their specific adverse reactions match within the prescribed time frame, it’s presumed that the vaccine caused the reaction and a capped but specified amount will be paid out for the injury (up to a maximum of $250,000 per death) plus some compensation for medical, rehabilitation, counseling, special education, lost wages, and attorney fees (attorney fees reimbursed regardless of win or loss), all within 240 days.  There are no peer juries in these vaccine court cases and the judge is literally called “Special Master” (check out the qualifications to become one).

Although it’s a $0.75 per dose excise tax levied on vaccine makers that is used to fund the vaccine court, it’s ultimately the taxpayers who wind up paying to legally immunize vaccine makers in this “structural quid quo pro” setup.

The flu vaccine is listed in this table, but it doesn’t specify what specific adverse reactions or time frame qualifies one for compensation claims.  Since it wasn’t clear what all that meant, I decided to do a Google search on “vaccine court flu injuries” and the first hit returned was by a Dr. Sherri Tenpenny, DO, where she had 60 embedded links to various vaccine court judgments linked to flu vaccines from JAN12 through AUG12.  So I started reading a few of these documents to obtain a flavor of what exactly goes on in these courts.

Sure enough, HHS (the respondent) always denies that the flu vaccine caused any injury, including death, but in these selected 60 cases over 8 months, they usually pay out something–GBS frequently occurs and and death doesn’t always pay out the most in terms of lump sums awarded, interestingly enough.  I have to say, these were some pretty interesting documents to read and they’re not very long or overly technical.

How Harmful Are Flu Vaccines: Tough to Say

Given this long, legally blessed marriage between government (HHS–parent agency for FDA and CDC) and vaccine makers, it’s tough to find any official evidence that flu vaccines cause any harm for obvious reasons, at least since this 1986 vaccine law was passed and implemented.  Just remember though that absence of evidence is not evidence of absence.  As a patient and potential consumer of flu vaccines, it’s good to know that if I were to suffer serious harm from it, my only potential recourse is to roll the dice in this federal vaccine court before a “special master” that can’t and will never admit that any vaccines cause any harm.

Of course, knowing this you have to ask yourself if any vaccine, specifically the flu vaccine, is advertised as totally or mostly harmless in exchange for a small benefit, why would vaccine makers require such strong legal immunity by the government and ultimately paid for at taxpayer expense?  What incentive in this quid quo pro arrangement is there for either vaccine manufacturers or the HHS (and its subsidiary agencies) to really make effectiveness and safety top concerns when the 1986 law that was recently upheld was designed to protect vaccine maker bottom lines?

In the interest of fully informed consent, perhaps vaccines in general should clearly convey such legal warnings in advance?

Thoughts on Cholesterol

Bacon Poster

I must confess: consistently eating low-carb, high-fat foods breaks two or three of the cardinal taboos of what many believe are part of a “heart healthy diet”: that is, I consume lots of saturated fats and cholesterol-rich foods from animal sources–preferably local, grass-fed or pasture-raised animal sources.

However, after improving and maintaining lots of measurable health markers for over 3 years now, sometimes I ponder part of Queen’s “Bohemian Rhapsody” (“Is this the real life?/Is this just fantasy?”) and reflectively ask myself “why am I not dead or suffering from cardiovascular disease (CVD)?”

Expectations if Cholesterol Caused CVD

If high cholesterol levels were the major cause of CVD as we are normally led to believe, then we should expect data to consistently and positively reflect the following three patterns on large scales:

(1) Those with high cholesterol levels should correlate with a higher incidence of all-cause mortality, specifically from CVD;

(2) Those with lower cholesterol levels should should correlate with a lower incidence of all-cause mortality, specifically from CVD; and

However, a number of such large scale studies have correlated lower cholesterol levels with increased mortality and higher cholesterol levels with increased longevity, especially among the elderly–e.g. Isehara StudyHonolulu Heart Program study, Norwegian HUNT 2 Study.

That is, those with the highest cholesterol levels tend to live longer than those with the lowest cholesterol levels.

The Framingham Heart Study is perhaps the longest (and most cited) cohort study (1948 – present?) to look at the associations of various health markers and health over time.  In 1992, 44 years after the project began, the study director, Dr. William Castelli, MD, wrote the following in an editorial to the Archives of Internal Medicine:

…Most of what we know about the effects of diet factors, particularly the saturation of fat and cholesterol, on serum lipid parameters derives from metabolic ward—type studies.2,3 Alas, such findings, within a cohort studied over time have been disappointing, indeed the findings have been contradictory. For example, in Framingham, Mass, the more saturated fat one ate, the more cholesterol one ate, the more calories one ate, the lower the person’s serum cholesterol….

There are other studies that could be listed here, but suffice it to say that such inconsistent findings would have to be reconciled or explained away to order to maintain the notion that higher levels of cholesterol cause or highly correlate with increased mortality over time, specifically from CVD.

(3) Interventions that effectively reduce high (specifically LDL) cholesterol levels–e.g. statin drugs–should correlate to lower incidence of all-cause morality, specifically from CVD, as compared to a control or untreated group.

In another post, I’ll discuss some thoughts on trying to lower cholesterol levels by eating low-cholesterol foods (hint: our body doesn’t seem to quite work this simply!).

But for now, one effective way to lower cholesterol levels is to do so artificially through statin drugs.  And so, for the sake of argument, let’s assume that propositions (1) and (2) hold.  Then it would seem to follow that if we could lower cholesterol levels (specifically LDL the “bad” type of cholesterol) through statins (which they seem to do pretty effectively), then there should be a lower incidence of all-cause mortality, specifically from CVD, in comparison to similar non-treated groups.

Here, however, the evidence has been underwhelming for the most part.  Below is a summary list of eight important randomized control trial (RCT) results of major statin drug intervention trials as they related to stroke preventions, from the Journal of Cerebral Blood Flow and Metabolism

Important Statin Trials

Most of the time, major media outlets will focus on the relative risk reduction (RRR) part of the study, which provides more material for sensational headlines by statistically making mountains out of molehills.

From a patient and potential statin consumer perspective, it’s far more important to know the absolute risk reduction (ARR) difference between the treatment group versus the non-treatment group and also the numbers needed to treat (NNT).  The ARR difference is important because I want to know how many bad outcomes (like death or CVD-related events, like stroke) are prevented with statin intervention compared to not taking the statin.  In these eight statin drug trials, this difference ranged from 0 to 1.6.  In this particular context, this means that there wasn’t too much significant difference between the groups that took statins and the groups that didn’t take statins as it related to stroke prevention over time.

The NNT is also important because I want to know how many people taking statins over time are needed to prevent just one stroke.  It’s a measure of how efficacious a drug is at doing what it’s intended to do over a certain period of time, and also a way to evaluate different statin efficacy rates against one another.  The lower the number the better.  As a patient, it lets me know my odds of benefiting from a particular type of statin intervention.  For example, if the NNT is 143, I have a 1 in 143 chance over some time of benefiting from treatment assuming I resemble the control and intervention groups–not great poker odds by any stretch.  As a reference point, taking 800 mg of ibuprofen has an NNT of about 1.6 within 4-6 hours in terms of achieving some measure of pain relief.

When looking at RCTs, which are the gold standard in terms of assessing the effectiveness of a single type of intervention (and no other confounding variables), knowing the ARR and NNT gives me a realistic picture of the potential upsides when deciding whether to take a statin or not.

In terms of cost-benefit analysis, I can then properly weigh my decision as patient and potential consumer against potential and known side effects (keeping in mind that no drug is a free lunch in terms of possible consequences), including fatigue, cognitive decline, muscle problems, increased diabetes risk, memory loss, and CoQ10 deficiency among others.

A more recent and controversial study is known as the JUPITER trial (2008).  JUPITER was another RCT but pre-selected for 17,000+ people without any history of heart disease who had normal to low LDL cholesterol levels but had high C-Reactive Protein (CRP) markers.  The study lasted short of two years.  A few peculiar things to note: (A) AstraZeneca, the manufacturer of Crestor, which was the drug used in the intervention group, funded the study, and (B) the lead author, who developed the device to measure CRP, also profits from its use.

Anyway, below is a table summarizing the major results of the intervention group against the placebo group:
JUPITER Trial

The ARR difference between intervention and placebo groups for fatal stroke and/or fatal heart attack ranged from 0.2% to 0.6% and the NNT was 25 over 5 years (or 95 over two years, depending on which time frame one wants to use).

Other Considerations

1. LDL cholesterol is not measured directly; instead on most lipid panels, it is estimated indirectly using the Friedewald equation (LDL = TC – HDL – [TG/5]) and seems to hold true if TGs are above 100 but below 400.  Total Cholesterol (TC), Triglycerides (TG), and HDL cholesterol are measured directly.  See Dr. Michael Eades’s, MD, post about this here.

It might cost a little extra, but I would totally ask for an LDL particle size (“Good” LDL particles are large and fluffy compared to “Bad” LDL particles that are small and dense) test if the standard lipid panel comes back with anything above 130 mg/dL AND the doctor wants to automatically prescribe a statin drug as a result.  The reason why is that very-low-carb, high-fat diets tend to raise HDL levels and dramatically drop TGs, both of which are measured directly, unlike LDL.

With an Excel spreadsheet, it’s easy to plug in the Friedewald equation to see how changing certain variables–most notably the TG–affects the calculated LDL number.

Excel Friedewald

2. I’ve always wondered how cholesterol guidelines are set and revised.  I mean who gets to decide that 100 mg/dL of LDL cholesterol is “optimal”?  And on what basis are such guidelines based upon for an entire population?  Is it done by competent but disinterested people?

Well, in 2004, the National Cholesterol and Education Program (NCEP) published an update based on 5 trials (3 of which are listed above–i.e. HPS, ASCOT, and PROSPER) to lower the recommended cholesterol guidelines for adults.

8 out of the 9 doctors who sat on this decision board had significant financial ties to various cholesterol drug manufacturers:

Dr. Grundy has received honoraria from Merck, Pfizer, Sankyo, Bayer, Merck/Schering-Plough, Kos, Abbott, Bristol-Myers Squibb, and AstraZeneca; he has received research grants from Merck, Abbott, and Glaxo Smith Kline.

Dr. Cleeman has no financial relationships to disclose.

Dr. Bairey Merz has received lecture honoraria from Pfizer, Merck, and Kos; she has served as a consultant for Pfizer, Bayer, and EHC (Merck); she has received unrestricted institutional grants for Continuing Medical Education from Pfizer, Procter & Gamble, Novartis, Wyeth, AstraZeneca, and Bristol-Myers Squibb Medical Imaging; she has received a research grant from Merck; she has stock in Boston Scientific, IVAX, Eli Lilly, Medtronic, Johnson & Johnson, SCIPIE Insurance, ATS Medical, and Biosite.

Dr. Brewer has received honoraria from AstraZeneca, Pfizer, Lipid Sciences, Merck, Merck/Schering-Plough, Fournier, Tularik, Esperion, and Novartis; he has served as a consultant for AstraZeneca, Pfizer, Lipid Sciences, Merck, Merck/Schering-Plough, Fournier, Tularik, Sankyo, and Novartis.

Dr. Clark has received honoraria for educational presentations from Abbott, AstraZeneca, Bristol-Myers Squibb, Merck, and Pfizer; he has received grant/research support from Abbott, AstraZeneca, Bristol-Myers Squibb, Merck, and Pfizer.

Dr. Hunninghake has received honoraria for consulting and speakers bureau from AstraZeneca, Merck, Merck/Schering-Plough, and Pfizer, and for consulting from Kos; he has received research grants from AstraZeneca, Bristol-Myers Squibb, Kos, Merck, Merck/Schering-Plough, Novartis, and Pfizer.

Dr. Pasternak has served as a speaker for Pfizer, Merck, Merck/Schering-Plough, Takeda, Kos, BMS-Sanofi, and Novartis; he has served as a consultant for Merck, Merck/Schering-Plough, Sanofi, Pfizer Health Solutions, Johnson & Johnson-Merck, and AstraZeneca.

Dr. Smith has received institutional research support from Merck; he has stock in Medtronic and Johnson & Johnson.

Dr. Stone has received honoraria for educational lectures from Abbott, AstraZeneca, Bristol-Myers Squibb, Kos, Merck, Merck/Schering-Plough, Novartis, Pfizer, Reliant, and Sankyo; he has served as a consultant for Abbott, Merck, Merck/Schering-Plough, Pfizer, and Reliant.

None of this is to say that statins are totally ineffective at avoiding CVD, but the benefits based on the available evidence I’ve seen make it seem like a high-risk, low-reward proposition when the upsides (via absolute risk reduction and numbers needed to treat) and side effects are carefully considered together.  And given today’s statin-mania, with pushes to treat healthy people with such drugs to include even children–which would sort of be like vaccination pushes but for CVD prevention–it’s good to be as sober with the data as possible until better evidence comes along.  After all, I have more of a stake in my own overall health than anyone else, or at least that’s how it should be.

Monday Night Racquetball 1

Rball 1

Tonight was a pretty good night for me in racquetball, a karmic refund from yesterday’s abysmal performance. There was one game that I forgot to record, of Jesse and I beating Adam “Mr. Semi Pro” Taylor and Loi. Also, tonight, we had a woman named Marie play with us for the first time. She said she used to compete in state tournaments several years ago and she’s pretty good.  She has a much better backhand than most of us men who play in the league.