Guest post: Grand Conspiracy Thinking.

Conspiracy thinking is another type of pseudoscience permeated with cognitive biases and logical fallacies. It is also fuelled by errors in perception and memory. The entire process is epistemologically crippled, involving circular reasoning and avoiding any possibility of refutation.

This can be counter-productive when extreme. Grand conspiracies involve many people, multiple organizations or agencies, acting for long periods of time.

A conspiracy theory is defined as a proposed plot by powerful people or organizations working together in secret to accomplish some goal (1, 2, 3). Conspiracy theories are not by definition false; indeed, many real conspiracies have come to light over the years.

Grand conspiracy theories tend to divide the world into three groups:

1. The conspirators themselves, they are powerful, have incredible resources and evil intentions. However, they make silly simple mistakes, the conspiracy theorists notice.

2. The conspiracy theorists have the ability to read between the lines and see the invisible hand of the conspirators at work.

3. Everyone else in the world, the naive people, the ones who are not engaged in the conspiracy and don’t see that it exists.

The Paranoid Style in American Politics (1966) by Richard Hofstadter is considered to be the first scholarly essay on conspiracy thinking. The essay looks at conspiracy thinking as a psychopathology and paranoid delusional disorder. However, because it is so common, conspiracy thinking is not fully explained by this.

Conspiracy thinking is a way to make sense of complex or mysterious events, when people feel that they lack control or are being victimized. In fact, we often lack detailed information about important political events or other events, thus opening the door even further to conspiracy thinking to fill the gaps in our knowledge with ideas or notions that are comforting in some way.

Another reason people believe in conspiracy theories is cognitive dissonance. When confronted with contradictory evidence for their beliefs, people protect their beliefs to overcome the dissonance of being wrong.

Also in play is hindsight bias, in which we tailor after-the-fact explanations to what already happened.

1. Pattern Recognition

Conspiracy theories are a form of pattern recognition, the cognitive form of pareidolia, a pattern imposed upon disconnected events or on random data, such as seeing an image in a stain, in a tree or a cloud. Conspiracy thinking is a hyperactive pattern detection.

Pattern recognition is also more common in response to feelings of powerlessness, pareidolia becomes more likely in research scenarios where subjects are made to feel more powerless. It can also channel feelings of anger.

 

2. Reality testing and pattern recognition

Pattern recognition is filtered through the reality-testing module in our brains. An apparent pattern is tested and the brain decides whether it conforms to our internal model of reality.

 

3. Confirmation bias

In confirmation bias, there’s the tendency to see all evidence as confirmatory. Sometimes even negative evidence tends to reinforce their certainty and confidence. Being a self-reinforcing effect, it makes conspiracy theorists stubbornly resistant to change.

 

4. Fundamental attribution error

Another cognitive trap, this time the tendency to blame other people’s behavior on internal, rather than situational, factors.

Conspiracy theorists think that all actions and outcomes are deliberate and intended, there’s no coincidence or chance. They ignore that people may be innocently responding to a situation rather than deliberately orchestrating every detail.

 

5. Closed-belief system

Conspiracy thinking quickly becomes a closed-belief system, a belief system insulated from external refutation from facts and evidence. All evidence against the conspiracy can be explained as being part of the conspiracy itself. Any lack of evidence can be explained as having been removed or covered up by the conspirators. In this way, they can explain away any dis-confirming evidence and the lack of any evidence. In essence, they render themselves immune to any possible refutation.

 

6. Shifting the burden of proof

They also render themselves immune to any burden of proof. They frequently shift the burden of proof onto others, when they have the burden of providing evidence in the first place.

 

7. Moving the goalpost

Shifting the burden of proof is combined with moving goalpost. No matter how much evidence is provided, it is never enough. For them every quirky must be explained to an arbitrary level of detail.

 

8. Anomaly hunting

Anomaly hunting is the process of looking for anything that seems out of the ordinary.

Complex historical events cannot be fully explained. This is because of the law of large numbers, the number of variables is so high that strange coincidences are will happen and are inevitable. There will always be anomalies to find, and conspiracy theorists use them to imply a dark conspiracy.

 

9. Naive assumptions

Conspiracy theorists often combine anomaly hunting with naive assumptions about how things should happen. Things are anomalous because they don’t fit with their idea of how events should have unfolded.

It is naive to assume that we can know with any detail what would result in any unprecedented event. Anything that does not fit their assumptions is an anomaly and evidence.

 

10. False dichotomy

The conspiracy theorist proposes a false dichotomy - that either the standard explanation of events is true or their conspiracy is true.

If they can call it into doubt and poke holes in the standard story, through naive anomaly hunting, then their conspiracy must be true. All they have to do is find anomalies, cast doubt and replace the standard story of events, with no burden of proof upon themselves. There are other possibilities, so they are offering a false choice.

 

11. Widening the conspiracy

Widening the conspiracy is another tactic to render a conspiracy immune to contradictory evidence.

Grand conspiracies tend to grow, the power and resources attributed to the conspirators grow like there is almost nothing they cannot do.

Eventually, the theories will simply collapse under their own weight. It becomes too great to be plausible and maintain coordination and secrecy.

Monological belief system

People who believe in one conspiracy theory tend to believe in many others. Psychologists at the University of Kent published a paper entitled “Dead and Alive: Beliefs in Contradictory Conspiracy Theories.”

“Conspiracy theories can form a monological belief system: A self-sustaining world-view comprised of a network of mutually supportive beliefs. The monological nature of conspiracy belief appears to be driven not by conspiracy theories directly supporting one another but by broader beliefs supporting conspiracy theories in general.” (4)

Belief in a particular theory is strongly predicted by belief in others, even if unrelated (2, 3, 5, 6).

Over time, the view of the world as a place ruled by conspiracies can lead to conspiracy becoming the default explanation for any given event - a unitary, closed-off world-view in which beliefs come together in a mutually-supportive network known as a monological belief system (3, 5, 6, 7).

The monological nature of conspiracism is driven not by conspiracy theories directly supporting one another but by the coherence of each theory with higher-order beliefs that support the idea of conspiracy in general. Conspiracism constitutes a monological belief system, drawing its coherence from central beliefs such as the conviction that authorities and officials engage in massive deception of the public to achieve their malevolent goals (4).

Another process may also be work, global coherence, which overrules contradictions, and “that everything happens for a reason….” (8).

 

Considerations

The existence of absurd and implausible conspiracy theories can be used to dismiss any questioning of conventional explanations. Some even hypothesize that the government is responsible for some of the worst conspiracy theories in order to cover up the real one.

Conspiracy theories are often very simplistic or one dimensional explanations.

Conspiracism has become a major subcultural phenomenon.

Clarke found a trend of increasing vagueness in these modern conspiracist communities (9).

When a conspiracy is hypothesized it must be treated like any other scientific hypothesis, we cannot assume the conspiracy is true, look for confirming evidence, protect it from dis-confirming evidence, we cannot shift the burden of proof and support the theory with anomaly hunting. That’s the pseudoscience of grand conspiracy theories.

The principle of the null hypothesis in science: any theory or hypothesis is false until proven otherwise. Thus, the default rule of thumb with conspiracy theories is that they are false.

The more complex the conspiracy and the elements involved, the less likely it is to be true.

References

1. Coady, D. (2006). Conspiracy theories: The philosophical debate. Aldershot: Ashgate.

2. Douglas, K.M., & Sutton, R.M. (2008). The hidden impact of conspiracy theories: Perceived and actual influence of theories surrounding the death of Princess Diana. Journal of Social Psychology, 148,210–222.

3. Goertzel, T. (1994). Belief in conspiracy theories. Political Psychology, 15, 731–742.

4. Michael J. Wood, Karen M. Douglas, and Robbie M.Sutton. Dead and Alive: Beliefs in Contradictory conspiracy Theories. Social Psychological and Personality Science January 25, 2012

5. Swami, V.,Chamorro-Premuzic, T., & Furnham, A. (2010). Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs. Applied Cognitive Psychology, 24, 749–761.

6. Swami, V., Coles, R., Stieger, S., Pietschnig, J.,Furnham, A., Rehim, S., & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Evidence of a monological belief system and associations between individual psychological differences and real-world and fictitious conspiracy theories. British Journal of Psychology, 102, 443–463.

7. Clarke, S. (2002). Conspiracy theories and conspiracy theorizing. Philosophy of the Social Sciences, 32, 131–150.

8. Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences,12, 435–502.

9. Clarke, S. (2007). Conspiracy theories and the Internet: Controlled demolition and arrested development. Episteme: A Journal of Social Epistemology, 4, 167–180.

Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998):175–220.

Goertzel, Ted. “The Conspiracy Meme.” CSI. http://www.csicop.org/si/show/the_conspiracy_meme

Klass, Philip J. The Real Roswell Crashed-Saucer Coverup. Amherst: Prometheus Books, 1997.

Novella, Steven. “Conspiracy Thinking: Skepticism’s Evil Twin.”

NeuroLogica Blog. http://theness.com/neurologicablog/index.php/conspiracy-thinking-skepticisms-evil-twin

Hofstadter, Richard. The Paranoid Style in American Politics (1966).

Posner, Gerald. Case Closed. New York: Anchor Books, 1994. The definitive book on the John F. Kennedy assassination.

Goldwag, Arthur. Cults, Conspiracies, and Secret Societies. New York: Vintage Books,2009.

Vankin, J., and J. Whalen. The Fifty Greatest Conspiracies of All Time. New York: Citadel, 1995.


For more information on Sérgio Fontinhas, see Big Fitness Project.

Guest post: Understanding scientific research [2]: Types and quality of evidence, by Sérgio Fontinhas.

Understanding scientific research [2]: Types and quality of evidence

In conducting scientific research it’s imperative to know how to evaluate the scientific literature and recognize the different nature and strength of scientific evidence available. Evidence is important to figure out what is true, but not to defend what you wish to be true.

Scientific beliefs and conclusions must be based on solid evidence, but because there are several kinds of evidence it’s not an easy task and not obvious how one should interpret evidence. There are different kinds of evidence with different strengths and weaknesses. Methods of balancing and comparing the different kinds of evidence are required.

The first distinction of scientific evidence is between experimental and observational evidence. The first step is how to assess an individual study. This has already been explored on part 1.

Then, all available evidence has to be balanced and compared.


1. Experimental versus Observational Studies

Experimental studies control as many variables as possible to measure a specific outcome. The goal is to isolate one variable so that the specific effects are determined.

The strengths of experimental studies include:
1. Controlling and isolating variables.
2. Quantitative in measuring some specific feature or outcome.
3. Statistical because there are comparison groups.

Weaknesses are also present:
1. Artefacts.
2. Interfering with a system may change its behavior.
3. May not be representative of real-world experiences.
4. May not be practical. There are certain kinds of experimental studies simply cannot be performed, for example exposing a person or group to toxins and anything that puts their well-being of life at risk.

Observational studies ideally do not intervene, they observe the world with no specific intervention. These are useful for correlations, to correlate a risk factor to a known disease for example. Or for palaeontology and archaeology, discovering and examining fossils, for astronomy observing the light from stars.

Strengths of observational studies:
1. Large amounts of data by observing what already exists.
2. Group comparisons.
3. Minimal intervention in the natural behavior of the system.

Weaknesses:
1. Do not control many variables.
2. Always subject to unknown variables.
3. Demonstrate correlation but cannot establish definitively cause and effect.

This two types of evidence, experimental and observational, are complementary and work together to provide different kinds of information with different strengths and weaknesses.


2. Examining the Data

Studies with only a few subjects (observational or experimental) are likely to be erroneous, because of the greater noise-to-signal ratio. Random effects to average out with large studies and sample sizes.

The statistical significance of results is often expressed as a P-value: the probability to get the results given the null hypothesis, which is the hypothesis that the phenomenon studied does not exist as opposed to the evidence establishing that the phenomenon does exist.

A p-value=0.05 means that 1 in 20 studies, where the null hypothesis is in fact true, will still give a positive result - 1 false positive result.

Barely significant results are not as compelling as highly significant results, because we can expect from chance alone the literature to be full of studies that are false positive.

However, statistical significance is not everything. A systematic flaw or bias in a study design can systematically bias the results in one direction (1).

The effect size is also needed, and we need to consider also how large the effect is. The smaller the effect size, the greater the probability that some subtle bias influenced the outcome, therefore tiny effect sizes are always tricky, even effect sizes that are right at the limit of our ability to detect them.

We also need to consider the drop-out rate of a study. A drop-out rate between 10-20% seriously reduces the reliability of the outcome. All data must be counted to avoid creating false results if only a subset of the data is counted. Selecting data can systematically bias results and make the outcome misleading.

Every single study must be systematically reviewed and then published, this is another way of counting all the data of all of the studies.

Statisticians do what’s called funnel plots, the scatter of results from different studies on the same question.

The literature if full of preliminary studies, where there’s large bias toward being falsely positive. However, as more rigorous studies are conducted, they begin to show the real effect.

As better studies are conducted over time effect sizes tend to shrink – this is called the decline effect. Even when the effect is real, the effect size is usually small in the more rigorous studies. For non-existent phenomena, the effect size shrinks to zero. The less rigorous studies tend to be more variable in their results and more shifted or biased towards the positive, however that gets worked out as studies become more rigorous.


3. Prospective and retrospective

A prospective study observes the behavior and outcome, conversely a retrospective study looks back at events and outcomes that have already occurred.

In principle prospective studies are more rigorous, this is because there are fewer confounding factors, are more systematic, and the samples are more representative. However for retrospective studies, there’s the potential for multiple bias.


4. Blinded and double blinded

In any rigorous study where the outcome of the results is be blinded, scientists are blinded to what they’re looking for in the intervention or in the control group. This reduces the subconscious researcher bias in the results. Experimental studies generally should be blinded in order to be reliable. Observational studies, on the other hand, can only be partially blinded.

The bias of the researcher can influence the results, in other words the results tend to be what they expect. Erroneous results disappear when proper blinding is put into place.


5. Controls

The controls must also be adequate. For example, what is the subject of the study being compared to? In medical trials an active control may obscure the comparison to the treatment, or the placebo may cause a negative outcome, making the experimental treatment seem artificially better. Perhaps the standard treatment to which a new treatment is being compared may be ineffective, making the new treatment seem more effective than it really is.

The control group being studied must also be representative of the population of interest. For example the 1936 Literary Digest poll failed mostly due to unrepresentative sample size and failure “to include the supposed core of Roosevelt’s support, the poor… and were excluded from the pool” (1). However others also pointed out “ both the sample and the response rate as being flawed”, and asserted that “the initial bias towards over-representation of Republicans in the sample was exacerbated by the fact that better-educated and wealthy people who tended to be Landon supporters were more likely to respond to the survey.” (1).


6. Examining the literature

Individual studies can be preliminary and flawed, or they can be rigorous and methodologically sound, but either way a single study is a single study. Few studies are so large, rigorous, and unambiguous in outcome that they can stand alone and can be considered definitive studies. Individual studies need to be put into the context of the overall research, or the published literature.

We must evaluate if an individual study has been replicated by independent labs and researchers. If so, we must evaluate if the results are consistent or mixed. In addition, did the results look at the same thing and control for the same variables?

When looking at any research question all literature must be considered and put it into context.


7. Publication bias

Publication bias is the tendency for researchers to push their results to be published when the results are interesting, positive, and somehow good for their career and reputation. Journal editors can have a bias toward publishing positive studies, potentially creating good press releases and draw attention to their journal.


8. Meta-analysis

Meta-analysis looks at many different studies addressing similar questions, it’s a study of studies which combines the results of multiple studies into a new statistical analysis for a greater statistical power.

However, there’s still new possibilities for bias. If the preliminary studies were poorly designed and biased, the meta-analysis will still reflect the bias of those preliminary studies. In fact, a meta-analysis a poor predictor of the outcome of later large, definitive studies, only about 60-70% (2).


9. Systematic reviews

Systematic reviews also look at all the evidence and consider the quality of each study. They look for patterns in the literature, consistency, replication, and relation to effect size and study quality.

Like meta-analysis, systematic reviews are also subject to bias. We need to look at which studies are included; the inclusion criteria; the methods used to find studies. All of these can affect the outcome of the systematic review.

The take home message is, all this design in scientific studies and methods of evaluating the literature is a way of compensating for our biases, flaws, fallacies. Evidence should and must be used to figure out what is true, rather than to defend what we already wish to be true.



References

1. Squire, Peverill. “Why the 1936 Literary Digest Poll Failed. Public Opin Q (1988) 52 (1): 125-133.

2. LeLorier, Jacques, Geneviève Grégoire, Abdeltif Benhaddad, Julie Lapierre, and François Derderian. “Discrepancies between Meta-Analyses and Subsequent Large Randomized, Controlled Trials.” New England Journal of Medicine 337 (1997): 536–542.

Novella, Steven. “Evidence in Medicine: Correlation and Causation.” Science-Based Medicine. http://www.sciencebasedmedicine.org/index.php/evidence-in-medicine-correlation-and-causation

Novella, Steven. “Evidence in Medicine: Experimental Studies.” Science-Based Medicine. http://www.sciencebasedmedicine.org/index.php/evidence-in-medicine-experimental-studies

Taper, Mark L.,and Subhash R. Lele. The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations. Chicago: University of Chicago Press, 2004.

Some fascinating healthcare statistics: Article by Ezra Klein.

Spotted on Twitter. From Is America better at treating cancer than Europe? , by Ezra Klein.

Healthcare rankings.

I highlighted Great Britain in green.

Cancer mortality.

Saturated fats Saturated fats Saturated fats.

The following video of Girls, Girls, Girls by Jay-Z is nowhere near as fun to watch as the video in Carbs Carbs Carbs.


George Henderson left the following comment. I think that the information in it deserves a bigger audience.

Saturated fats seem to get the blame for everything nowadays. "Saturated fats clogged my arteries". "Saturated fats gave me cancer". "Saturated fats stole my job". O.K, I've done that joke before.

There are saturated fats, there are saturated fats, there are saturated fats, there are saturated fats, there are saturated fats and there are saturated fats. Saturated fats are an ester of Glycerol (a 3-carbon alcohol) and three saturated fatty acids (SFA's). There are roughly six categories of SFA's.

1) Short chain SFA's such as Acetic acid, Propionic acid, Butyric acid (found in butter and also what soluble fibre ferments into in the colon) and Caproic acid.
2) Medium chain SFA's such as Caprylic acid, Capric acid, Lauric acid and Myristic acid.
3) Long chain SFA's such as Stearic acid.
4) SFA's behaving like Palmitic acid.
5) Odd chain SFA's such as Pentadecylic acid and Margaric acid.
6) Very long chain SFA's such as Behenic acid.

See http://en.wikipedia.org/wiki/List_of_saturated_fatty_acids

In foods, the above SFA's are associated with different things.
1) and 2) don't get associated with much polyunsaturated fatty acids (PUFA's), e.g. dairy and tropical nuts.
3) and 4) are more likely to be associated with long-chain PUFA's, e.g. meats, poultry, temperate nuts.
5) is associated with CLA and not much PUFA's, e.g. dairy from grass-fed animals.

See also Siri-Tarino et al, Forests & Trees and "Eureka!" moments.

How a B.Sc.(Hons) in Electronic Engineering is relevant to Diet & Nutrition.

The human body regulates various processes using negative feedback loops. Here's blood glucose regulation.

From http://www.studyblue.com/notes/note/n/ch-47-chemical-signals-in-animals/deck/3085387

Here's a generic Hypothalamus-Pituitary-X Axis loop, where X may be thyroid, adrenal, gonadal etc.

From http://www.studyblue.com/notes/note/n/ch-47-chemical-signals-in-animals/deck/3085387

Electronic Engineers understand how negative feedback systems work, such as phase-locked loops & amplifiers.

Negative feedback control systems can overshoot, especially if there is a delay in the feedback path that's longer than the rise time of the input step.

An example of this is the first-phase insulin response. Loss of the first-phase insulin response occurs in over-fat people who are hyperinsulinaemic. Without the first-phase insulin response, there is a delay between an increase in blood glucose and an increase in insulin secretion. A rapid upwards step in blood glucose (say, from eating a high-GL meal) causes an overshoot in insulin secretion, resulting in blood glucose that goes lower that it was to start with. This stimulates hunger pangs, as per Return of hunger following a relatively high carbohydrate breakfast is associated with earlier recorded glucose peak and nadir.

See also Blood Glucose, Insulin & Diabetes.

People shouldn't be too quick to write-off the knowledge of an Electronic Engineer who's delving into the mysteries of the human body.

The Conflation Game.

Li-i-ife, is the name of the game, and I wanna play the game with you.....


People have been "grinding my gears" by conflating carbohydrates with sugars. All sugars are carbohydrates, but not all carbohydrates are sugars. See Carbs Carbs Carbs. to find out about the five basic different types of carbohydrates.

Krauss et al has done it again. In Separate effects of reduced carbohydrate intake and weight loss on atherogenic dyslipidemia, at the bottom of Table 1 is "carbohydrate, 50% simple and 50% complex". The carbohydrates consisted of half sugars and half starches.

The effect of such a sugary diet is as follows:-

The percentage of pattern B (small, dense) LDL particles increases significantly in proportion to the percentage of Dietary "carbohydrate". The implication of this study (also A very-low-fat diet is not associated with improved lipoprotein profiles in men with a predominance of large, low-density lipoproteins ) is that high-carb, low-fat diets are atherogenic.

This is not on.

A high sugar diet is atherogenic, but carbs from new potatoes (especially if eaten cold), Basmati rice (especially if eaten cold), sweet potatoes, beans & whole fruits aren't.

Guest post: Denialism as Pseudoscientific Thinking.

In pseudoscience there’s a subtype called Denialism. Denialism seeks to deny an established science and violate multiple principles of logic, and scientific methodology, this is mostly because of a priori beliefs and preconceptions. Typically the same cognitive and logical errors are committed in denialism reasoning.

The whole process starts with a desired conclusion, that a generally accepted scientific or historical claim is not true. Denialists have ideological reasons, and engage in motivated reasoning, rationalizing away the undesired claim.

In essence and practical terms, they work backward from their desired conclusion, filling in justifications.

1. Moving the goalposts

In moving the goalposts, they always demand more evidence for a claim, even if currently available. However when that burden of evidence is met, the goalposts are moved and more evidence is demanded.

They may use vagueness in defining a certain term to move the goalpost away from any possible dis-confirming evidence.

2. Unreasonable demand for evidence

Because science has gaps, they explore them as if it the specific scientific theory being discussed is invalid or not well established.

Let’s take the example of HIV denial. Deniers often demand a single study or scientific paper establishing HIV as the cause of AIDS. However, it is not established by a single study but rather by a large body of evidence.

In scientific reasoning we must see if the gaps are slowly being filled, and if predictions are met, and if it fits together with other lines of evidence, observational or experimental.

If a theory has been going around in circles and not progressing, that is a strong indication of pseudoscience.

3. Pointing out disagreements

Disagreements within a discipline are explored, often small details, as if the science in question is not solid.

4. Denying entire categories of evidence

Another strategy the narrowing of evidence that may count as “scientific”. The most common is using the logical fallacy of confusing correlation with causation.

Correlation is not the same as causation, not necessarily anyway. Correlations need to be used properly, and multiple correlations can triangulate a specific causal relationship observed in a correlation. Epidemiology is based on correlations and observational evidence, if they were invalid the entire field simply would vanish.

They can even deny all historical sciences such as astronomy, geology, or even forensics.

5. False dichotomy

This is an argument from ignorance. If a version of events is not true then the alternate claim or version must be. However, they rarely provide positive evidence for their alternate claim.

6. Campaign of Doubt

Little factoids can be gathered and taken out of context. The goal is to sow doubt, uncertainty, and distrust, focusing on apparent inconsistencies, or gaps. However in healthy skepticism we consider all the evidence in the proper perspective, and even though knowledge is incomplete, reliable conclusions can be achieved.

7. Conspiracy theory

As a last resort comes the conspiracy theory, claiming that the scientific evidence itself is fraudulent, a grand conspiracy. This tactic allows them to dismiss all the evidence and rationalize it away.



Grant, John. Denying Science. Amherst: Prometheus Books, 2011.

Novella, Steven. “More on God of the Gaps.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/more-on-god-of-the-gaps

Novella, Steven. “Skepticism and Denial.” The NESS. http://www.theness.com/index.php/skepticism-and-denial

Specter, Michael. Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives. London: Penguin Press, 2009

Tokuno, Hajime. “Holocaust Denial.” The NESS. http://www.theness.com/index.php/holocaust-denial


For more information on Sérgio Fontinhas, see Big Fitness Project.

Guest post: Science versus Pseudoscience.

Pseudoscience is so flawed that it cannot be considered legitimate science. Of course it is common to claim that one’s beliefs are scientific, but mostly they are not.

Pseudoscience lacks the true method of science and goes way beyond just a few errors, the methods themselves are so flawed that makes the theory suspicious.

Between the two extremes of science and pseudoscience there is a gray zone, but legitimate science and pseudosciences can still be identified. The denial of this two extremes in the continuum, is a false continuum logical fallacy, or philosophically called the demarcation problem.

Features of Pseudoscience

1. Motivated reasoning

The most prominent feature of this pathological science is working backward from desired results, or motivated reasoning. The result is that they make evidence fit into preconceived notions. They use biased logic and cherry-picked evidence in order to defend a desired conclusion. There’s no concern and effort to prove their own theories wrong.

This relates to the congruence bias, testing one’s own theory by looking for positive evidence and cherry-picked evidence.

2. Burden of proof and confirmation bias

They will only look for confirming evidence, avoid dis-confirming evidence, and may engage in special pleading and shifting the burden of proof.

In confirmation bias, they look for supportive evidence for their own desired conclusions, choosing only the evidence that supports their own theory, irrespective of quality, negative evidence.

3. Anecdotal evidence

Anecdotes are uncontrolled, or ad-hoc observations, and they are not systematic. They rely on confirmation bias and recall bias.

Low-grade evidence is often favored no matter how implausible it may be.

Emotional appeal is another typical tactic among pseudoscientists who try to defend their statements, claiming what people say is more important than actual numbers on paper.

Pseudoscientific belief may even be based upon a single case or observation, preliminary evidence, or even a single anecdote. This is the hasty generalization logical fallacy.

Pseudoscientific principles may also be based upon a philosophical idea, not been empirically tested or developed as a scientific theory.

4. Grandiose claims (Galileo syndrome)

This involves grandiose claims based upon preliminary evidence. Far-reaching claims overturn entire portions of well-established science, using very little research or tiny bits of evidence.

5. Alternative science

In extreme cases, pseudoscience leads to alternative science, all of science is replaced with an alternative version.

6. Absolute claims

Pseudoscientists make bold claims that are often absolute and go way beyond the evidence. Pseudoscientists offer simple answers to complex questions, a theory of everything where one tiny casual source is used to explain the entire universe, if it comes to that.

7. Hostility

Pseudoscientists generally cannot accept criticism and avoid the scientific community. They claim being victim of a conspiracy and stay away from mainstream science and community.

8. Vagueness

Pseudoscientists use vague terms and words to obfuscate, so they can shift the definition around, use it in different ways at different times when it suits them, to confuse others and avoid explaining their point. Vague terms such as “information” or “energy” are often used with no specificity as in a scientific discussion.

9. Stagnation

Pseudosciences fail to progress, and tend to be stagnant. They are ad nauseam trying to establish their theory rather than build a body of evidence for it.

10. Anomaly hunting

Anomaly hunting is yet another common feature in which they search for anomalies trying to establish a conclusion, which does not seek to refute or explore other alternatives.



Nickerson, Raymond. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2, no. 2 (1998): 175–220.

Novella, Steven. “Anomaly Hunting.” NeuroLogica Blog. http://theness.com/neurologicablog/index.php/anomaly-hunting

Pigliucci, Massimo. Nonsense on Stilts: How to Tell Science from Bunk. Chicago: University of Chicago Press, 2010

Shermer, Michael, The Borderlands of Science: Where Sense Meets Nonsense. New York: Oxford University Press, 2001.

Gardner, Martin. Fads and Fallacies in the Name of Science. Mineola: Dover Publications, 1957
Shermer, Michael. Why People Believe Weird Things. New York: Henry Holt/Times Books, 1997.


For more information on Sérgio Fontinhas, see Big Fitness Project. 

Another tasty analogy.

Here's a tasty analogy.

From http://grannychoe.com/recipe3_Soup.php

In Ultra-high-fat (~80%) diets: Fat storage, and a delicious analogy, I analogised the effect of carbohydrate consumption on mean serum glucose level with the effect of fat consumption on mean serum triglyceride level. Here's another one.

Chronic excessive consumption of carbohydrates relative to what are being burned results in excessive fat synthesis in the liver, resulting in excessively-high fasting serum triglyceride level, which is harmful.

Chronic excessive consumption of fats relative to what are being burned results in excessive cholesterol synthesis in the liver, resulting in excessively-high fasting VLDL, LDL & IDL level, which is harmful.

Seems legit.

Siri-Tarino et al, Forests & Trees and "Eureka!" moments.

Here's Fig. 2 from Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease:-

Risk ratios and 95% CIs for fully adjusted random-effects models examining associations between saturated fat intake in relation to coronary heart disease and stroke.

The above "Forest" plot has a subtotal RR of 1.07 (95% CI 0.96 1.19). The overall conclusion is that there's no association between saturated fat intake and the RR for CHD. Hmmm.

I looked at the data in Table 3. Of the 16 studies contributing to the CHD results, only 3 of them specify high sat fat intakes over a wide range. The results from these 3 studies are as follows:-

Pietinen et al: RR=0.93 (95% CI 0.6, 1.44).
Mann et al: RR=2.77 (95% CI 1.25, 6.13).
Boniface et al: Pooled RR = 1.37 (95% CI 1.17, 1.65).

The results from Pietinen et al are statistically-insignificant (95% CI values are way above & below 1) with an overall slight protective effect. The results from Mann et al have a RR >> 1 with both 95% CI's >1 and the results from Boniface et al have a RR >1 with both 95% CI's >1.

Other studies either have sat fat intakes varying from very low to low, or specify mean/median sat fat intakes without values for highest & lowest tertiles/quartiles/quintiles etc. Other studies have results that are statistically-insignificant.

However, there are some studies that show a slight protective effect of small amounts of sat fats. How come?

Thanks to George Henderson, I had a "Eureka!" moment. He posted a link to Dietary intake of saturated fat by food source and incident cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis.

Here's Fig. 1 from that study.

HRs and 95% CI's of CVD risk according to quintiles of energy-adjusted SF from different sources (n = 5209).

The Meat SF plot has a net positive slope (bad news, but the range of intake is so small that the results are probably due to chance), the Butter & Plant SF plots are random, but the Dairy SF plot has a net negative slope (good news). Dairy saturated fats in amounts of up to 10g/day are protective against CHD. As the Dairy sat fat intake is too small to have a significant effect on lipids, what's the mechanism? I think that it's Vitamin K2. See Chowdhury et al, More forests & more trees and more "Eureka!" moments with cheese.

When you average out the results from all studies, the result is null. This is data dilution statistics.

EDIT: See also Study: Saturated Fat as Bad as Sugar!

I'm NOT a lipophobe, I'm a very naughty boy!

First, postprandial triglycerides again. From Fasting Compared With Nonfasting Triglycerides and Risk of Cardiovascular Events in Women, here's a plot of HR for future CHD vs TG's at various times after eating.

Hazard ratio (HR) and 95% confidence interval (CI) for highest vs lowest tertiles of triglyceride level (see Table 3 for values), adjusted for age, blood pressure, smoking, hormone use, levels of total and high-density lipoprotein cholesterol, diabetes mellitus, body mass index, and high-sensitivity C-reactive protein level.

Notice how the HR falls with increasing time from last meal. As TG's ≥12 hours after eating are a surrogate for Insulin Resistance (IR) and the HR is only 1.04 (95% CI 0.79 - 1.38), this strongly suggests that IR is not a significant factor.

It's been suggested that IR might increase PP TG's in the 2 - 4 hour period due to impaired clearance. According to Fig. 3B in Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism, TG clearance in healthy men doesn't significantly start until after 4 hours has elapsed. Therefore, an impairment in TG clearance isn't going to make a significant difference to TG level in the 2 - 4 hour period.

Second, the reason why I'm having to repeat myself is due to Cholesterol: Do chylomicrons clog your arteries? (2), where I've been called "my resident lipophobe". As I drink Gold Top milk (5.2g of fat/100mL) and eat pork including belly slices (you know, those strips of pork with a lot of fat on them), I'm being attacked for something that I'm not.

What I'm criticising is dietary extremism. Eating fats in foods is fine by me, but eating sticks of Kerrygold butter and/or dumping loads of butter and/or MCT oil into coffee to achieve "Nutritional Ketosis" is not a good idea. Anyway, here's an amusing spoof on Bulletproof coffee.

Reversing type 2 diabetes, the lecture explaining T2D progression, and how to treat it.

Julianne Taylor of Paleo & Zone Nutrition posted the following excellent lecture on Facebook:-

Eating Through The Myths: Food, Health and Happiness - Taylor, Prof. R., Berlin, 28-Sep-12

Salient points:

1) It's a chronic calorie excess (of carbohydrates and/or fats) that causes problems.
2) Motivation, motivation, motivation!
3) Both diet and exercise are important. See Move More: Solutions to problems.
4) You can't outrun your fork. See The 5th Myth of Modern Day Dieting: You Can Outrun Your Fork.
5) Underlying Insulin Resistance needs to be addressed. See Insulin Resistance: Solutions to problems.

For more information on Prof. Taylor's work, see Reversing Type 2 Diabetes.

Carbs Carbs Carbs.

The following video of Boys Boys Boys by Sabrina Salerno is NSFW in parts.


Carbohydrates seem to get the blame for everything nowadays. "Carbohydrates made me fat". "Carbohydrates burned-out my pancreas". "Carbohydrates raised my blood glucose". "Carbohydrates raised my blood triglycerides". "Carbohydrates stole my job". O.K, I made the last one up!

If carbohydrates are responsible for all of these bad things, then how come a diet of only potatoes had the opposite effect? See 20 Potatoes a day.

Also, Blue Zone populations eat a diet with a high percentage of total energy (%E) from carbohydrates. See Low serum insulin in traditional Pacific Islanders--the Kitava Study and The Kitava Study. The Kitavans eat ~70%E from carbohydrates, ~20%E from fats and ~10%E from proteins. They don't eat a significant amount of Western junk-foods.

Maybe it has something to do with the type of carbohydrates and with what they're eaten. In A very-low-fat diet is not associated with improved lipoprotein profiles in men with a predominance of large, low-density lipoproteins , (emphasis, mine) "The very-low-fat, high-carbohydrate experimental diet was designed to supply less than 10% of energy from fat (2.7% saturated, 3.7% monounsaturated, and 2.6% polyunsaturated), with 75% from carbohydrate (with equal amounts of naturally occurring and added simple and complex carbohydrate) and 15% from protein." Simple carbohydrates are sugars.

The experimental diet which did bad things contained 37.5%E from sugars. I declare shenanigans!

There are simple carbs, there are simple carbs and there are simple carbs. In the previous post, the graph of plasma triglycerides after an OGTT showed that 100g of glucose had no significant effect on plasma triglycerides over a 6 hour period. If it had been 100g of fructose, there would have been a significant increase in plasma triglycerides. Galactose is taken-up by the liver and has minimal effect on blood glucose, but I don't know its effect on plasma triglycerides.

There are complex carbs, there are complex carbs and there are complex carbs. Overcooked starch is high in amylopectin which is highly-branched, which means that it hydrolyses rapidly into glucose which gives it a very high glycaemic index. Raw & refrigerated potato starches have very low glycaemic indices, due to the presence of amylose, or other resistant starches. Rice contains a mixture of starches which varies with rice type, cooking time and subsequent refrigeration.

EDIT: There are oligosachharides e.g. FOS. There are polysaccharides e.g. inulin and soluble fibre/fiber e.g. cellulose.

Although overeating sugars containing fructose & starches that rapidly hydrolyse into glucose makes the liver fatty, overeating fats also makes the liver fatty. See Pathogenesis of type 2 diabetes: tracing the reverse route from cure to cause. It's the chronic over-consumption of junk-foods (high in sugars and/or starches and/or fats), not just carbohydrates, that causes over-fatness and other health problems.

Ultra-high-fat (~80%) diets: Fat storage, and a delicious analogy.

Fat storage:

Here's a plot of mean (±SEM) plasma insulin concentrations during an oral-glucose-tolerance test (OGTT) when preceded by either a high-fat (▪) or a high-carbohydrate (□) evening meal and during an oral-fat-tolerance test (OFTT) when also preceded by either a high-fat (•) or a high-carbohydrate (○) evening meal.

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

100g of glucose produces a large spike in insulin concentration and 40g of fat produces no significant spike in insulin concentration. According to Gary Taubes' insulin hypothesis of obesity, in the absence of a significant spike in insulin concentration, fat cannot be stored.

Here's a plot of mean (±SEM) plasma triacylglycerol concentrations during an oral-fat-tolerance test (OFTT) when preceded by either a high-fat (•) or a high-carbohydrate (○) evening meal (from the previous post).

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

Average plasma triacylglycerol concentration over 0 to 360min is ~1.3mmol/L (~116mg/dL in US units).

Plasma triacylglycerol concentration falls to baseline between 240min and 360min. OGTT's and OFTT's are performed with the subjects at rest for the duration of the test.

Referring to It's all in a day's work (as measured in Joules) , at rest the subject is burning ~1kcal/min with ~95% of it coming from fat, making a fat-burning rate of ~0.11g/min.

At a fat-burning rate of ~0.11g/min, it would take ~360min for plasma triacylglycerol to fall to baseline if the 40g of fat from the OFTT was only being burned and not being stored. As shown above, it only takes ~120min to fall to baseline. Therefore, fat from the OFTT that isn't burned is stored in ~120min in the absence of a significant insulin spike. Q.E.D.

A delicious analogy:

Here's a plot of mean (±SEM) plasma glucose concentrations during an oral-glucose-tolerance test (OGTT) when preceded by either a high-fat (▪) or a high-carbohydrate (□) evening meal and during an oral-fat-tolerance test (OFTT) when also preceded by either a high-fat (•) or a high-carbohydrate (○) evening meal (from the previous post).

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

The OGTT (100g of glucose) produces a large spike in plasma glucose concentration which lasts for ~210min before returning to baseline. Higher plasma glucose concentrations glycate more than lower plasma glucose concentrations. Average plasma glucose concentration over 0 to 360min is higher with the OGTT than with the OFTT, therefore there is more glycation damage with the OGTT than with the OFTT. Don't regularly consume 100g or more of glucose!

Here's a plot of Mean (±SEM) plasma triacylglycerol concentrations during an oral-glucose-tolerance test (OGTT) when preceded by either a high-fat (▪) or a high-carbohydrate (□) evening meal.

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

Average plasma triacylglycerol concentration over 0 to 360min is ~1.0mmol/L (~89mg/dL in US units).

Although the plasma triacylglycerol concentration after consuming a high-carbohydrate evening meal is slightly higher than after consuming a high-fat evening meal, the two plots above are essentially flat, indicating that none of the 100g of glucose consumed was turned into fat by de novo lipogenesis (DNL) within 6 hours.

As discussed in the previous post, higher plasma triacylglycerol concentrations are more atherogenic than lower plasma triacylglycerol concentrations. Average plasma triacylglycerol concentration over 0 to 360min is higher with the OFTT than with the OGTT, therefore there is more atherogenicity with the OFTT than with the OGTT.
Don't regularly consume 40g or more of fat!

An interesting study that involved humongous fat consumption was Response of body weight to a low carbohydrate, high fat diet in normal and obese subjects , which used up to 600g of fat/day. It's possible to lose weight on an ultra-high-fat diet, but average plasma triacylglycerol concentrations would have been extremely high. Fasting TG's reduce on an ultra-high-fat diet, probably due to suppression of endogenous TG synthesis by exogenous TG intake.

Ultra-high-fat (~80%) diets: The good, the bad and the ugly.

The good:

Here's a plot of mean (±SEM) plasma glucose concentrations during an oral-glucose-tolerance test (OGTT) when preceded by either a high-fat (▪) or a high-carbohydrate (□) evening meal and during an oral-fat-tolerance test (OFTT) when also preceded by either a high-fat (•) or a high-carbohydrate (○) evening meal.

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

An OGTT (100g of glucose dissolved in water) causes a large disturbance in blood glucose level for up to 2 hours. Ditto for insulin (see Fig. 2).

An OFTT (40g of fat as cream) doesn't cause a significant disturbance in blood glucose level. Ditto for blood insulin (see Fig. 2).

The bad:

Here's a plot of mean (±SEM) plasma triacylglycerol concentrations during an oral-fat-tolerance test (OFTT) when preceded by either a high-fat (•) or a high-carbohydrate (○) evening meal.

From Extended effects of evening meal carbohydrate-to-fat ratio on fasting and postprandial substrate metabolism

An OFTT (40g of fat as cream) causes a significant rise in blood triacylglycerol (a.k.a. TAG a.k.a. triglycerides a.k.a. TG's) level for up to 4 hours. Is this a problem? Definitely, maybe. From Cholesterol And Coronary Heart Disease , "Cholesterol-depleted particles oxidise faster than large cholesterol-rich ones." Chylomicrons are triglyceride-rich, cholesterol-depleted, as that's the composition of the fat in the diet.

The ugly:

Here's evidence that excessive postprandial TG's raise the risk factor for CHD:- See Fig. 1 in Fasting Compared With Nonfasting Triglycerides and Risk of Cardiovascular Events in Women.

Here's more evidence that postprandial saturated fatty TG's raise the risk factor for CHD:- See Postprandial triglyceride-rich lipoproteins promote invasion of human coronary artery smooth muscle cells in a fatty-acid manner through PI3k-Rac1-JNK signaling.

See also Postprandial triglyceride-rich lipoprotein changes in elderly and young subjects.,
Effect of a single high-fat meal on endothelial function in healthy subjects.,
Postprandial lipemia: emerging evidence for atherogenicity of remnant lipoproteins.,
Alimentary lipemia, postprandial triglyceride-rich lipoproteins, and common carotid intima-media thickness in healthy, middle-aged men.,
Evidence for a cholesteryl ester donor activity of LDL particles during alimentary lipemia in normolipidemic subjects.,
Association of postprandial hypertriglyceridemia and carotid intima-media thickness in patients with type 2 diabetes.,
Postprandial hypertriglyceridemia impairs endothelial function by enhanced oxidant stress.,
High-energy diets, fatty acids and endothelial cell function: implications for atherosclerosis.,
Impact of postprandial hypertriglyceridemia on vascular responses in patients with coronary artery disease: effects of ACE inhibitors and fibrates.,
[Influence of postprandial hypertriglyceridemia on the endothelial function in elderly patients with coronary heart disease].,
Impact of postprandial variation in triglyceridemia on low-density lipoprotein particle size.,
Association between fasting and postprandial triglyceride levels and carotid intima-media thickness in type 2 diabetes patients.,
[Correlation of lipemia level after fat loading with manifestation of atherosclerosis in coronary arteries].,
Postprandial hypertriglyceridemia and carotid intima-media thickness in north Indian type 2 diabetic subjects.,
Association between postprandial remnant-like particle triglyceride (RLP-TG) levels and carotid intima-media thickness (IMT) in Japanese patients with type 2 diabetes: assessment by meal tolerance tests (MTT).,
Postprandial lipemia and remnant lipoproteins., 
Elevated levels of platelet microparticles in carotid atherosclerosis and during the postprandial state.,
Postprandial metabolic and hormonal responses of obese dyslipidemic subjects with metabolic syndrome to test meals, rich in carbohydrate, fat or protein.,
Atherosclerosis, diabetes and lipoproteins., 
Clinical relevance of non-fasting and postprandial hypertriglyceridemia and remnant cholesterol.,
Post-prandial hypertriglyceridemia in patients with type 2 diabetes mellitus with and without macrovascular disease.,
A hypertriglyceridemic state increases high sensitivity C-reactive protein of Japanese men with normal glucose tolerance.,
CD36 inhibitors reduce postprandial hypertriglyceridemia and protect against diabetic dyslipidemia and atherosclerosis., 
[Trends of evaluation of hypertriglyceridemia -from fasting to postprandial hypertriglyceridemia-].,
The effects of dietary fatty acids on the postprandial triglyceride-rich lipoprotein/apoB48 receptor axis in human monocyte/macrophage cells.

See also What Is the Significance of Postprandial Triglycerides Compared With Fasting Triglycerides? , Uncovering a Hidden Source of Cardiovascular Disease Risk and Postprandial Lipoproteins: The storm after the quiet!

A counter-argument is that the subjects in the above studies were eating carbohydrate, and that postprandial TG's aren't atherogenic if you're not eating much carbohydrate. Definitely, maybe. In the absence of carbohydrate, there is still glucose in the blood, thanks to the liver. Also, some carbohydrates don't spike blood glucose (or fructose) level. It's pure speculation that the subjects in the above studies had high blood glucose at the same time as high postprandial TG's. As Insulin Resistance/Metabolic Syndrome and/or a high-sugar diet raise fasting TG's, and there was no significant association between fasting TG's and the risk factor for CHD, this suggests that the subjects had no significant metabolic derangement and were not eating excessive amounts of sugar.

Also, some people's LDL levels go extremely high on a very-low-carb, very-high-fat diet. See Lipidaholics Anonymous Case 291 Can losing weight worsen lipids? A very high LDL level results in a high LDL particle count, even if the particles are large (Type A). A high LDL particle count is a strong risk factor for CHD. See also Fig. 1 in Some Metabolic Changes Induced by Low Carbohydrate Diets.

It's possible to get Coronary Artery Calcium (CAC) scans, to measure the amount of calcified plaque in coronary arteries. While a high CAC value means lots of plaque, a zero CAC value doesn't necessarily mean zero plaque, as young people and people with a high Vitamin K2 intake don't have significant calcification. See Stenosis Can Still Exist in Absence of Coronary Calcium.

Update 26th July 2014: See Page 10 of  HIGH CARBOHYDRATE DIETS: MALIGNED AND MISUNDERSTOOD - Nathan Pritikin. Read the text, starting with:-
"Could such a cream meal precipitate an angina attack because the oxygen-carrying capacity of the blood is lowered?"
The answer appears to be "Yes."

Bray et al shows that a calorie *is* a calorie (where weight change is concerned).

Continued from Everyone is Different, Part 3.

EDIT: I made an error in stating that all of the extra calories came from fat, in the fat overfeeding phase. Thanks to commenter CynicalEng for pointing that out. It doesn't change the conclusion at all.

At 01:17 on 6th June, I was told during a Facebook discussion:-
"Nigel Kinbrum - read this please.
Bray, et al. Shows that a Calorie is Not a Calorie and that Dietary Carbohydrate Controls Fat Storage.
Perhaps you'll learn something from a real expert who teaches metabolism to medical students at the largest medical school in the country."

So I did.

At 02:22, I replied:-
"Thanks for that. I read Feinman's blog post about Bray et al http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777747/ some time ago.
There's a fundamental error in Feinman's analysis. As LeonRover pointed out in his comment http://feinmantheother.com/.../bray-et-al-shows-that.../...
In Diets:- "Absolute carbohydrate intake was kept constant throughout the study."
Also, in COMMENT:- "The extra calories in our study were fed as fat, as in several other studies, and were stored as fat..."
Oh, whoops! That may be why it was rejected by the editor."

Here's Figure 6 from Bray's study.

From http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777747/

Some Definitions:-

LBM = Lean Body Mass
FM = Fat Mass = Body Fat

Weight change = (LBM change + FM change)

Weight change varies from ~+3.5kg (@ +2,500kJ/d) to ~+9.1kg (@ +5,900kJ/d).

(Maximum weight increase)/(minimum weight increase) = 2.6
(Maximum kJ/day increase)/(minimum kJ/day increase) = 2.36

∴ A calorie *is* a calorie (where weight change is concerned) ± some inter-personal variation.
∴ Insufficient protein can result in LBM loss (this is bad).
As LBM has a lower Energy Density (~400kcals/lb) than FM (~3,500kcals/lb),  LBM loss can increase weight loss, when in a Caloric Deficit.
See The Energy Balance Equation, for a simple explanation, and The Dynamics of Human Body Weight Change, for an incredibly complicated one!


I was rather chuffed when Alan Aragon left the following comment at 04:34:-
"Nigel is correct. From Bray et al's text:
"The extra calories in our study were fed as fat, as in several other studies [33,34], and stored as fat with the lower percentage of excess calories appearing as fat in the high (25%) protein diet group. The higher fat intake in the low protein group probably reduced nutrient absorption (metabolizable energy) relative to the other groups and this would have brought the intake and expenditure closer together in this group.""

Feinman has deleted his blog post. However, his post I Told George Bray How to do it Right is still there. I believe that Dr. George A. Bray M.D. sort-of did it right.

Dr. George A. Bray used a "weight maintenance formula" in all three groups for the weight maintenance phase. He then changed the formula in all three groups to low-P, med-P and high-P formulas, for the fat overfeeding phase. Carbohydrate grams remained constant in all three groups for all phases, but additional fat grams were fewer in the high-P group than in the low-P group, for the fat overfeeding phase.

I would have used the low-P, med-P and high-P formulas for the weight maintenance phase and for the fat overfeeding phase, to equalise the additional fat grams in all three groups.

Saturday silliness.

Now that I have resumed annoying people discussing diet & nutrition on the internet, the following chart should be used by non-English people, to allow them to understand what I write.

From http://www.thepoke.co.uk/2011/05/17/anglo-eu-translation-guide/

For a laugh, I created the superhero of Diet & Nutrition "The Nigeepoo".

Using http://www.heromachine.com/heromachine-2-5-character-portrait-creator/

Have a nice weekend!

False dichotomies: cot'd.

I'm talking about the "What causes Z, X or Y?" & "What is best, X or Y" type statements.

From http://johnbarban.com/fitness-vs-fatness-a-false-dichotomy/

Some people believe that hormonal disruption causes obesity, rather than energy excess. The vast majority of people who are overweight or obese weren't born with hormonal disruption. It's years of chronic energy excess (see Determinants of the Variability in Human Body-fat Percentage for the many reasons causing it) that make people too heavy/fat than is healthy. Once too heavy/fat than is healthy, various hormones become disrupted, causing even more energy excess. Therefore, the cause of obesity is not one thing or another, it's both (plus lots of others), which is why reversing it is so difficult.

On Peter D's blog, the title reads "You need to get calories from somewhere, should it be from carbohydrate or fat?" I say "Both. And some protein. And a bit of alcohol, too!" And I know that I shouldn't start sentences with And.

It's been a while since I posted a video of me singing. Here's one from February this year.