Death of Happiness Greatly Exaggerated

We are a “clicking” nation. We measure how important ideas are by the veracity of readers’ “like” “share” or comments. In response to this reality, newspapers’ headlines are dramatic and “clicking good”. Becoming increasingly common however, is that such click-baiting headlines rarely reflect the content of the news items. Human foibles promote the constant search for dramatic new insight, in contrast to the slow dribble from the fountain of knowledge.

A case in point is a paper published in the esteemed medical journal Lancet this year. The news reports went ecstatic with this news, within a few days reports spread across the globe with 402 newspaper and magazine headlines reporting: “Million Women Study: Happiness does not guarantee a long life” (Times UK-Dec 10, 2015); “Happiness is not the key to a long life” (Independent Online-Dec 10, 2015); “Happiness Doesn't Bring Good Health, Study Finds”(New York Times-Dec 9, 2015); “Happiness Doesn't Help You Live Longer” (The Atlantic-Dec 9, 2015); “Being happy doesn't make you live longer, research says” (The Independent-Dec 9, 2015); “Is Happiness Really Linked To Longevity? Maybe Not, Study Finds” (Forbes-Dec 10, 2015). In one short article, and a contagion of newspaper headlines, a century of work that has consistently shown the importance of happiness for longevity have been ignored or discarded. We “clicked” happiness to death. But this might have been exaggerated.

The original paper [1], conducted by Bette Liu with the Faculty of Medicine, University of New South Wales, Australia and her colleagues, had an interesting methodology. The short abstract—on which most newspapers must have based their compelling news reports—concluded: “In middle-aged women, poor health can cause unhappiness. After allowing for this association and adjusting for potential confounders, happiness and related measures of wellbeing do not appear to have any direct effect on mortality.” Being miserable is cool and it does not hurt you. But before you throw away your Prozac, lets examine what these researchers did and see if we can come up with different and more articulate interpretation of the data.

This study was based on the Million Women Study, a study of 1.3 million UK women in their 50s who went for a mammogram—recruited between 1996 and 2001—looking at future cancer incidence after taking Hormone Replacement Therapy. In the past, the results based on this database have been controversial. The overall findings have found lifestyles, habits or behaviors as remembered and recalled by participants in their youth have less an affect on cancers, heart disease or other illnesses than present lifestyle choices.  Basically arguing that—other than radiation—the present is more important than the past. This goes against the whole field of epigenetic studies and how lifestyle and diet choices have long lasting effect, sometimes across multiple generations.  But one of the problems with this database is that they are dealing with recall and self-perception. How we recall events tend to come in-line with our present reality. 

Returning to this specific article on happiness, heralding the importance of happiness, surprisingly the authors found that in all the results, being happy most of the time faired better on ALL variables. No exceptions. While being unhappy was associated with higher mortality—even after adjusting for age, which influences probability of death—by 30 to 40%. Despite these dramatic correlations, the authors still conclude that: “After allowing for this association and adjusting for potential confounders, happiness and related measures of wellbeing do not appear to have any direct effect on mortality”. The fact that the authors were able to whittle away all of the very positive correlates of happiness to leave a shriveled edifice of happiness, holding no predictive power, attest to how skilled the authors are at playing with statistics. Within a clinical database, the authors undress happiness until its emptiness is exposed. But what is happiness without its expression?

It is not the statistics that is questionable but the authors’ methodology. This type of analysis is referred to as “kitchen sink” analyses. Chuck everything in and see what comes out. Results are not theory driven but motivated by spurious and random associations. Eliminating correlates of happiness away—within a very limited, clinical database—happiness becomes irrelevant. But happiness is an collection of evaluations of how content we are in life. It is made up of individual components, with our evaluation of our health forming a main aspect of our happiness.

Because the authors found that happiness was related to all positive variables, they did something very strange. They adjusted happiness. Within logistic regression—which tests the effect of a condition/s (or independent variable) on a yes or no outcome (dependent variable, which in this case was dead or alive) you adjust variables by modifying the outcome to match the condition and thereby eliminating the effect of one independent variable on the dependent variable. This is important because you can isolate an individual variable and see how it behaves regardless of all other variables. Which is what the authors did for happiness.  First they broke up the groups into three main groups and then they started eliminating variables sequentially to see which one will mute the effect of happiness. They did this by diluting their construct validity, and then by reverse engineering happiness. Let me explain.

The first methodological fault is massaging their definition of “unhappy”. Out of 719,671 women with a median age 59 year, 39% reported being happy most of the time while the majority (44%) reported being happy usually, while the final group was defined as rarely reporting happiness. But this is not accurate. This final group was composed of three very distinct categories of people who reported being either happy sometimes, rarely or never. Far from being a homogenous group this category is a subjective potpourri of a group of sensible people who report that they do sometimes feel happy, combined with—according to the DSM-V—a clinically diagnosable group who are likely depressed and report never to be happy. So this is a strange mixture of people grouped together and called “unhappy.”  Methodologically, the authors should have selected only the never feel happy group. They are a distinct group. But by lumping all three categories together they lost construct validity. We do not know what they are comparing the happy group against.  When they refer to the “unhappy” group a proportion of these are happy sometimes.

They continued to dilute the construct validity by excluded the first five years of follow-up and women who had already had heart disease, stroke, lung disease, or cancer. We do not know why these people were excluded but it is likely that these very ill women were likely to be the least happy and the most clinically depressed (based on their own data of who these women are because the strongest associations with reported unhappiness were treatment for depression or anxiety and reporting only fair or poor general health). By eliminating them the authors got rid of the negative outliers, further diluting the construct to include people closer to the average. By including “sometimes happy” people in the group of “unhappy” people, and then eliminating the extremely unhappy people, what the authors did is to dilute the construct of “unhappy” to produce a group that is closer to the average.  

The second methodological fault is the reverse engineering of happiness. After finding that happiness is positively correlated with all healthy indicators, the authors proceeded to strip away these variables. This is known as adjusting the data. The authors adjusted the data for a number of factors. In regression analyses such adjustments create ceteris paribus, a Latin term meaning “all else being equal.” So when you adjust for variables you even out—eliminate the affect of—that variable. Practically you are throwing these variables out of the effect. Such statistical techniques are important when you want to see if one variable is important by itself accounting for the effect of all the other variables separately. But in this case we have to question the number of variables that were adjusted to minimize happiness. Theoretically in psychology, happiness is not a stand-alone construct but an omnibus construct reflecting a number of individual components. If you eliminating these recorded expressions—and the Million Women Study database is limited in how happiness is recorded—then there are few variables that are correlated. Being happy was correlated with increasing age, having fewer educational qualifications, doing strenuous exercise, not smoking, living with a partner, and participating in religious and other group activities. 

Only when the authors eliminated ALL the correlated of happiness that exist in their database did happiness become an emaciated variable with no meaning. The authors first adjusted only for age. Then they continued to adjust for region of residence at recruitment including employment, car ownership, home ownership, and household overcrowding, college and pre-college education, living with a partner, whether they are obese, perform strenuous exercise, smoke, drink alcohol of one drink a day, participation in religious or other group activity. None of these activities diminished the effect of happiness, which tells us that happiness is expressed in people who are not defined by any of these categories. The only variable that seems to mimic or act as a proxy for happiness is self-rated health (in their Table 2).

In summary the three adjustments that got rid of the happiness factor:

Eliminate all the really depressed and sick people. Eliminating over 125 769 women who at baseline already had heart disease, stroke, cancer, or chronic obstructive airways disease. These excluded women who had three times the death rate. Again, it is very likely that this group of women were the most depressed and unhappy.
Diminish the effect of older adults who are normally happy. By adjusting for age we are reducing the effect of happiness. We know that the older we become the happier we are. Such consistency data that has economists interested in psychology because it determines economic behavior. Despite this adjusting for age, happiness still emerged as a resilient factor in reducing mortality. Adjusting only for age, unhappiness remained associated with 25-33% increase in death.
Only by getting rid of self-rated health did the effect of happiness completely vanished. Translated this means if we eliminate the importance of how healthy or unhealthy participants felt, then it does not matter how miserable you are in determining your mortality.

But after adjustment for self-rated health, treatment for hypertension, diabetes, asthma, arthritis, depression, or anxiety, lifestyle factors—including smoking, deprivation, and body-mass index— unhappiness was not associated with mortality from all stress or lack of control.

In psychology, happiness is relatively stable, while unhappiness is more variable [2]. Similarly the authors of this study reported that there was some instability in happiness figures, especially from being unhappy to becoming happy a year later.  While only 2% who reported being happy most of the time at baseline changed to being unhappy at follow-up, 5% of women who reported being unhappy at baseline reported being happy most of the time a year later. This is a gain of 3 % per year (difference between becoming happier to becoming sad). From their own study the results show that very year, there is an improvement on happiness of 3%.

Dariusz Leszczynski, a Polish cell biologist wrote in the Washington Times Communities, Oct 3, 2013 that “The Million Women Study has shoddy exposure design leading to shoddy results and ending with shoddy conclusions.”  Applying a database to study relationships other than what the database was original developed for is not inherently bad science. But when there are complex constructs such as happiness, that are not fully understood, having a million or more women who went in for a mammogram might not be a representative group to generalize from. The limitation in external validity is significant.

Happiness is a central emotional indicator that brings our body and mind in balance. It is one of the main predictors of mortality that even economists and actuaries apply to adjust their mortality forecasts based on the present level of happiness and self-rated health. If there is one objective in life it is to be happy, everything else is peripheral. Attempting to summarily dismiss a century of research [3]—that has been trying to understand the meaning of happiness and longevity—needs to be questioned.

Citations

[1] Liu, B., Floud, S., Pirie, K., Green, J., Peto, R., Beral, V., & Million Women Study Collaborators. (2015). Does happiness itself directly affect mortality? The prospective UK Million Women Study. The Lancet.

[2] Veenhoven, R. (1994). Is happiness a trait?. Social indicators research,32(2), 101-160.

[3] Lucas, R. E. (2007). Personality and the pursuit of happiness. Social and Personality Psychology Compass, 1(1), 168-182.

© USA Copyrighted 2015 Mario D. Garrett

The Preservation of Human Energy through Aging

E = MC²

Einstein’s iconic formula defines the theory of relativity. We think of this formula mainly in quantum physics and space exploration, but it also applies to our body. The theory specifies that a body that weighs say 68kg (150 lbs.) will have 6,111,535,215,410,359,952 Joules of energy. This is 6,111 Petajoules (PJ) which translates to 1,697,500 giga watt hour. In comparison, 31.5 PJ is the electrical annual output of a large power plant, while 210 PJ is equivalent to about 50 megatons of TNT. This is the amount of energy released by thirty Tsar Bomba, the largest man-made nuclear explosion ever.

One person is a walking universe of restrained energy. We can only access a small percentage of this energy so most of this energy remains inaccessible. But our biology knows how to utilize part of this energy. And one way that it uses energy is by storing it in fat. It is a reserve that our biology does very well. This balance is based on the first law of thermodynamics applied to an open system. It is an open system because energy can be added to the system by ingesting food. Energy that is not used by the body is stored in the form of glycogen, lipids (mainly in the form of fatty acids), and protein. Unlike glucose, fat can be stored in large quantities for extended lengths of time. As a result, fat mass is the main long-term energy storage mechanism of the human body.

Surprisingly, we are built with two systems of energy: glucose and ketones. In ketosis, once we run-out of glucose, we switch to our fat for energy. We break down fat into fatty acids, and those fatty acids which the liver transforms into ketones, which are then used as the energy source by the brain, muscles, and other tissues. Ketosis occurs through high fat intake, fasting, or reduction of carbohydrates.

The average adult has as much energy stored in fat as a one-ton battery. Fat is a very good system when there is energy shortage, but becomes problematic when there is an abundance of food since the body is excellent at storing fat. Especially since the body protects any loss of fat. Weigle et al (1988) reported that the body is aware of how much energy is stored. So for someone who lost a lot of stored energy (say after dieting) the body uses 18% less daily energy than those of the same relative body weight conducting the same activity, as those who had never dieted. This is a clever energy conservation system that we see in action better in older age.

The average human, at rest, produces around 100 watts of power. This equates to around 2000 kcal (kilocalorie =4184 joules) of food energy, which is why your recommended daily intake of calories is around 2000 kcal. Over periods of a few minutes (or a few hours in the case of trained athletes), we can comfortably burn 300-400 watts of energy—and in the case of very short bursts of energy, such as sprinting, some humans can output up to 2,000 watts.

As we get older this equation changes dramatically. We become less efficient. The idea that aging is simply an attrition of energy is one of the oldest theories in gerontology starting  with Aristotle (384-322 BC) and was later adopted by the Romans, Muslims and Western European medical establishments.  It became the basis for our early understanding of how the human body works.  Essentially, Aristotle held that the human body was filled with four humors: black bile, yellow bile, phlegm, and blood. Any imbalance in these four humors resulted in diseases and disabilities. Aging is caused by the drying out and cooling of these humors. This idea had a wide following, and involved numerous hot baths and saunas in order to maintain our wetness and heat. 

Surprisingly, older adults do have slightly lower temperatures. The 98.6° F benchmark for body temperature comes from Carl Wunderlich—a 19th-century German physician—is not accurate for older adults.  In 2005,  Irving Gomolin from Winthrop University Hospital in New York, found that older people have lower temperatures than younger adults. In a study of 150 older people with an average age of 80-plus, they found an average temperature of 97.7°. What is fascinating is longer you survive the lower your body temperature…or is it the other way round?  Although most have seen lower temperature as correlated with mortality, lower temperature might be a protective function, knowing that the body likes to conserve energy. And that is exactly what a 2006 research study found. An Italian researcher Bruno Conti at the Scripps Research Institute showed that a decline in body temperature is beneficial. The study found that mice that had lower core body temperatures lived 12% (male) to 20% (female) longer than mice with higher core body temperatures. The difference in temperatures between "cold" and "normal" mice was 0.5-0.9 F (0.3-0.5 C), which is the same difference between the average young person and the average older adult. Perhaps the body is conserving energy by reducing its operating temperature and therefore reducing metabolic rate, free radicals and stress on the system.

The science behind this anomaly is just now becoming clear. One of the known ways to increase longevity is to restrict calorie intake—eating fewer calories. Caloric restriction increases lifespan in all sorts of animals.  Several studies have reported that animals on reduced calorie diets also had a lowering of core body temperature. By conserving energy and reducing temperature, our body is also slowing the aging process. One interpretation from this is that the reason older adults may have lower body temperature is not because they are dying, but because it is our body’s way of preserving energy, resulting in living longer. In the Baltimore Longitudinal Study of Aging, men with a core body temperature below the average (median) lived significantly longer than men with body temperature above the average. But how does Caloric Restriction (CR) result in lower temperature and conserving of energy?

After Caloric Restriction (CR) was initially discovered in 1935 in mice, it has been tested with varied organisms and it has been shown to increase the lifespan in yeast, insect, and in non-human primates.  In humans CR is still undergoing testing, although initial results suggest prolongation of life as well as prevention of age-related diseases are likely outcomes. The mechanism seems to emulate the genetic work of life prolongation, in that the CR elicits a hormesis event—a low level stressor that stimulates a positive response. The system is believed to involve genes that become active when stress occurs. These genes are referred to as epigenetic. Epigenetics are genes once thought to be “junk” genes, but now we are finding that they can be switched on or off. In most cases they are triggered by environmental factors. The best way to study the effect of genetics on longevity is to look at twins. Monozygotic twins, those that split from a single egg, have nearly similar genetic makeup at birth. In contrast, twins that have a different egg (dizygotic) only share the same level of genotype as with any other siblings.

More than three decades ago, Cook and his associates published a study looking at the onset of dementia among monozygotic twins who were both affected by Alzheimer's dementia. In one case study, dementia began in her late 60s, while in the other twin the onset of dementia was at age 83. Subsequent studies confirm that although monozygotic twins might both have the disease, how they express them and when they express the disease might differ. The difference was used attributed to the environment. In support of this interpretation recent studies are blurring the difference between genetics and the environment. In 2000 biologists Randy Jirtle and Robert Waterlanda from Duke University modified the expression of a gene—called agouti gene--which made mice fat, yellow and prone to cancer and diabetes.  These mice did not live very long. After an intervention however, these researchers produced young mice that were slender and healthy, evading their parents' susceptibility to cancer and diabetes living to an active old age. The researchers virtually erased the effect of the agouti gene.  Remarkably, the researchers modified the expression of this gene not by altering the mice genes, but by changing the moms' diet.  Feeding the mother a diet rich in onions, garlic, beets, and in food supplements often given to pregnant women the researchers provided a chemical switch that reduced the agouti gene's harmful effects.

These foods—known as methyl donors, folic rich foods—enhance or diminish gene activation and gave birth to a whole new science of epigenetics.

The same changes in our genetic expression are seen throughout our life. With older adults expressing more variance in their genetic expression. In 2012, Jordana Bell of King's College London and colleagues looked at the genes of 86 sets of twin sisters aged 32 to 80, and repeated with another 44 sets of younger twins aged 22 to 61, and discovered that 490 genes linked with ageing showed signs of epigenetic change. In particular, among this epigenetic expression were four genes that relate to cholesterol, lung function and maternal longevity. What is phenomenally interesting is that these changes are not just brought about by diet and methyl rich donors, but also by such lifestyle factors such as smoking, environmental pollution, stresses, and attitude.

There are many studies in this area of how genetic manipulation changes longevity through the conservation of energy. The three seminal research studies  all have one thing in common and it is to do with how the body reserves energy through growth hormones. The first type is a classic experiment by Michael Rose who began manipulating the life spans of fruit flies by allowing them to reproduce only at late ages. The subsequent progeny of flies evolved longer life spans and greater reproduction over the next dozen generations. If our parents delayed producing us, then our body seems to know to conserve energy in order to make the body live longer to enable it to pass on its genes at this delayed period. 

The second type of experiment uses examples from nature, which were then emulated in the laboratory at U.C San Francisco by Cynthia Kenyon. Here they chemically knocked out certain genes in flatworms, the gene daf-2 which partially disables receptors that are sensitive to two hormones—insulin and a growth hormone called IGF-1. This had the effect of nearly doubling the flatworms’ lifespan. These long-lived worms looked and acted younger than their control group, implying that extending the lifespan also extends healthy life. By conserving energy by weakening insulin and a growth hormone, the flatworms lived longer.

Then there is the third type of genetic observation with mice, in particular the work done by Richard Miller, and his infamous mouse called Yoda (who is now deceased.) Like other dwarf mice, Yoda had a natural genetic mutation that obstructs the production of growth and thyroid hormones. Dwarf mice tend to grow to only about a third the size of normal mice, which helps them live about 40 percent longer. The common denominator in all these experiments and observations is that stunted growth and a conservation of energy correlates with increased lifespan. Surprisingly there are two inclusive and complimentary theories that can explain these findings.

The theory of Antagonistic Pleiotropy argues that some genes have contradictory effects at different age. Genes which might enhance your reproductive success at a younger period in our life—genes that increase testosterone in men, resulting in more muscle mass and masculine secondary sexual characteristics—may at an older age have detrimental effects on survival later in life—in the testosterone example it is the elevated risk of cancer. Natural selection tends to favor these kinds of genes because they maximize fitness and the passing of genes while the higher mortality occurs in post-reproduction stage will have little impact on increasing the number of offspring. The second theory is that of Disposable Soma which states that given that there are finite resources to maintain and repair cells and organs, the body conserves energy and protects itself just long enough so that we are able to pass on our genes.

As with all genetic work there are many confounders. There is no direct translation from the genotype to the phenotype. The environment can intervene as is being shown with epigenetics. Even if we accept that stunted growth might preserve energy and improve lifespan, other factors might negate such gains.  And that is the case with a southern Ecuador group where more than 250 individuals are thought to have Laron syndrome—IGF-1 deficiency in primary growth hormone—caused by a mutation in the growth hormone receptor gene with affected individuals growing to less than 4 feet tall. Although Laron patients appear to be protected against developing cancer, this apparent protection does not translate to a longer lifespan due to trauma and alcoholism. There is a schism between lifespan and theoretical lifespan…human behavior. The ability of the body to preserve energy might be negated by our negative behavior.

© USA Copyrighted 2015 Mario D. Garrett

Further Readings

Aguiar-Oliveira M.H., et al. (2010). Longevity in untreated congenital growth hormone deficiency due to a homozygous mutation in the GHRH receptor gene. J Clin Endocrinol Metab.95(2),714–21.

Bartke A &  Brown-Borg H. (2004). Life extension in the dwarf mouse. Curr Top Dev Biol, 63:189–225.

Calabrese V, Cornelius C, Cuzzocrea S, Iavicoli I, Rizzarelli E Edward J & Calabrese EJ. (2011) Hormesis, cellular stress response and vitagenes as critical determinants in aging and longevity.Molecular aspects of medicine 32, no. 4 .279-304.

de Cabo, Rafael, Didac Carmona-Gutierrez, Michel Bernier, Michael N. Hall, and Frank Madeo. "The Search for Antiaging Interventions: From Elixirs to Fasting Regimens." Cell 157, no. 7: 1515-1526. (2014).

Finch, CE (1998). Variations in senescence and longevity include the possibility of negligible senescence. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 53.4: B235-B239.

Finch CE & Malcolm CP (1996). Maximum life span predictions from the Gompertz mortality model." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 51.3: B183-B194.

Fotios, D & Kirkwood TBL (2005). Modelling the disposable soma theory of ageing." Mechanisms of ageing and development. 126.1: 99-103.

Gomes N et al. (2011). Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination. Aging cell. 10.5: 761-768.

Greider C W & Blackburn EH. (1985). Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell. 43.2: 405-413.

Hayflick L & Moorhead PS (1961).The serial cultivation of human diploid cell strains. Experimental cell research. 25.3: 585-621.

Hayflick L (1998). How and why we age. Experimental gerontology 33.7: 639-653.

Kenyon C. (2012). Could a hormone point the way to life extension?. elife. 2012;1:e00286. doi: 10.7554/eLife.00286. Epub .  Oct 15.

Kenyon C. (2011). The first long-lived mutants: Discovery of the insulin/IGF-1 pathway for aging. Philos Trans R Soc Lond B Biol Sci. 366, 9-16.

Kenyon C et al. (1993). A C. elegans mutant that lives twice as long as wild type. Nature 366.6454: 461-464.

Keyfitz, N. (1977). Applied Mathematical Demography. 1st ed. New York: John Wiley.

Laron, Z., Kopchick, J. (Eds.) (2011). Laron Syndrome - From Man to Mouse. Lessons from Clinical and Experimental Experience. Springer.

Manton KG. (1986). Past and future life expectancy increases at later ages: Their implications for the linkage of chronic morbidity, disability, and mortality.  Journal of Gerontology 41(5), 672-681.

Miller RA (2012). Genes against aging." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 67.5: 495-502.

Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL et al. (2011). Quantification of the effect of energy imbalance on bodyweight. Lancet, 378: 826–837.

McGuire, MR, Wing, R, & Hill JO (1990). The prevalence of weight loss maintenance among American adults  Int J Obes, 23, 1314–1319.

Tesco, G, et al. (1998). Growth properties and growth factor responsiveness in skin fibroblasts from centenarians. Biochemical and biophysical research communications 244.3: 912-916

Olovnikov AM (1996).Telomeres, telomerase, and aging: origin of the theory.Experimental gerontology 31.4: 443-448.

Pobojewski, S. (2014). World's oldest mouse reaches milestone birthday. The University Record. May 1.

Rauser, Casandra L., et al. (2006). Evolution of late‐life fecundity in Drosophila melanogaster." Journal of evolutionary biology 19.1: 289-301.

Rose, Michael R., et al. "The effects of evolution are local: evidence from experimental evolution in Drosophila." Integrative and Comparative Biology 45.3: 486-491.(2005).

Lauren RW & Polotsky AJ (2012). Can we live longer by eating less? A review of caloric restriction and longevity. Maturitas 71(4), 315-319.

Steuerman R., Shevah O., Laron Z (2011). Congenital IGF1 deficiency tends to confer protection against post-natal development of malignancies. Eur J Endocrinol. 164(4):485–9.

Thomas DM, Martin CK, Heymsfield S, Redman LM, Schoeller DA, Levine JA. (2011) A simple model predicting individual weight change in humans, Journal of Biological Dynamics, 5:6, 579-599, DOI: 10.1080/17513758.2010.508541

Weigle, D. S., Sande, K. J., Iverius, P. H., Monsen, E. R., & Brunzell, J. D. (1988). Weight loss leads to a marked decrease in nonresting energy expenditure in ambulatory human subjects. Metabolism, 37(10), 930-936.

Weiss EC, Galuska, L. Kettel Khan L Gillespie, C, Serdula MK (2007). Weight regain in U.S. adults who experienced substantial weight loss, 1999–2002. Am J Prev Med, 33 (1), 34–40.

  

The Eugenics Period in Research

Research Domain Criteria (RDoC) is a new classification of diseases—nosology-- championed by the U.S. National Institute of Mental Health (NIMH). It was especially promoted by the NIMH then director Thomas Insel. Insel has now migrated to Google Life Sciences which in the Google empire has become a full-fledged member of Mountain View's Alphabet Inc., and taken on a new name: Verily, a for profit health company.

RDoC baptism coincided with the publication of the DSM-5 in 2013, and heralds a radical diagnostic departure by relying exclusively on biomarkers—biological indicators. The implicit assumption being that behavioral/mental/clinical disorders are manifestations of biological/neurological disorders. Bad behavior is nothing more than shorted circuits in the physical system. Finding the bad circuits will fix the problem. The explicit emphasis of RDoC is to “yield new and better targets for treatment.” [1]  While demoting the importance of understanding the disease, it elevates the search for a cure. There are emerging criticism of this new nosology [2] [3] [4], but what remains untold, is how RDoC is gaining legitimacy.

RDoC’s biological determinism was promoted by the success of how easy it was for the public and scientists to believe that Alzheimer’s disease was determined by biomarkers. The history of Alzheimer’s disease laid the foundation for a new way of biological determinism that has not been seen since the height of the eugenics movement in 1923 when the American Eugenics Society was founded. But this emphasis on biology is unfounded. There is no evidence that biology exclusively determines Alzheimer’s disease or many other mental disorders. But the illusion was made possible by the acceptance of such an association—that Alzheimer’s disease is purely a neurological disease.

Historically only tenuous evidence separated Alzheimer’s disease from senile (old age)dementia. Alois Alzheimer’s observation—shared by many of his contemporary researchers—was that the biomarkers were not unique either for Alzheimer’s disease or among younger people. But the plaques and tangles were elevated as a unique disease classification by Emil Kraepelin—Alzheimer’s supervisor at the Munich clinic. From its inception, Alzheimer’s disease was promoted as a unique disease because of a context that; 1) promoted biological psychiatry, 2) encouraged competition between Munich and Prague laboratories, 3) the belief that genes and biology determine behavior—eugenics, and 4) ageism, the idea that old age invariably results in diminished capacity but that a similar disease among young people is more noteworthy. These socio-political factors supported the legitimacy of accepting that the plaques and tangles were indicators of Alzheimer’s disease—an association that remains unsupported to this day. RDoC’s new way of biological determinism follows from this illusionary success in defining Alzheimer’s disease as biological. Let’s follow the story further.

The story of Alzheimer’s disease took a new turn in the US with the inception of the National Institute on Aging. The first director of the NIA, Robert Butler—when reflecting on NIA's strategy of emphasizing  neurobiological research—confessed that such political machinations reflect the “health politics of anguish.”  Politics have been germane to the context of Alzheimer’s disease from the beginning. Although the NIA adopted Alzheimer’s disease as its war banner—a war to get enhanced funding from Congress—for this war to be won, the NIA needed to create a push and a pull. The pull came from creating an epidemic, while the push came from massive public pressure on Congress. 

To achieve this "push" the NIA co-opted the mandate of the Alzheimer’s Association—originally the Alzheimer’s Disorder and Associated Dementias—in order to focus on research. In turn the "pull" was created by "creating" an epidemic  which pure Alzheimer’s disease could not generate because it was exclusively diagnosed only among a small group of younger adults.  So in 1975—in contradiction to Alzheimer, Fischer, Perusini, Bonfiglio, Kraepelin and Pick, who all believed in a dementing disease that afflicted younger adults—the classification of Alzheimer’s disease was singlehandedly, and without consultation, modified to included senile dementia. "We should like to make the suggestion, simplistic as it may be, that we should drop the term 'senile dementia' and include these cases under the diagnosis of Alzheimer's disease" [5]  Overnight Alzheimer’s disease subsumed the much larger group diagnosed with senile dementia.[6] Older adults became co-opted in a war for the "pull" of research dollars . The following year this new approach was accompanied by a more detailed study on prevalence [7]. 

Overnight Alzheimer’s disease became the sixth highest cause of death in the United States, becoming an instant epidemic. With its name—Alzheimer’s disease—came all the attributes of a real neurological disease, while abandoning senile dementia meant dumping the muffled reference to old age. Although this was a shrewd political move it increasingly meant that the meaning of Alzheimer’s disease expanded and broadened, resulting in an increasingly muddled and confused meaning. Such lack of clarity was intentional.

By broadening the meaning, Alzheimer’s disease instantaneously became the king of all dementias.  That year, in 1976, Alzheimer’s disease became the most common form of dementia, being diagnosed in over 60% of all dementia cases—followed by Vascular dementia, Dementia with Lewy bodies, Fronto-temporal dementia, Korsakoff syndrome, Creutzfeldt-Jakob disease, and HIV-related cognitive impairment. The rare forms which occur in 5% of cases relate to corticobasal degeneration: Huntington's disease, multiple sclerosis, Niemann-Pick disease type C, normal pressure hydrocephalus, Parkinson's disease, posterior cortical atrophy and progressive supranuclear palsy. Different types of dementias might have different and very specific causes. Arnold Pick saw dementia as “. . . a mosaic of localized partial dementias. . .” [8]  Disregarding specificity of dementias,  Alzheimer’s disease became the focus for the fight to cure all dementias. It attracted billions of research dollars and captivated an entourage of highly talented researchers, dedicated to advancing biochemical and neurological science.

The ongoing story of how RDoC could have gained momentum came in 2011 when the NIA and Alzheimer’s Association (AA) published the Alzheimer’s disease guidelines. These guidelines created separate stages of the disease from pre-clinical to Mild Cognitive Impairment (MCI), to early and advanced stages of dementia. Although promoted as research guidelines, no suggestions were offered to improve research methodology, identify anomalies, formalize and standardize instrumentation, define MCI, establish causality, develop hypotheses, generate theoretical predictions, discuss and assimilate alternative interpretations, summarize research updates or propose a road map for future research—all recommendations that normally would be expected in research guidelines.

Nevertheless, the guidelines promoted a powerful agenda—but it was a political rather than a research agenda. Despite the lack of evidence for this approach—inherent in the Amyloid Cascade hypothesis [9]—the NIA/AA guidelines effectively allowed the pharmaceutical industry to experiment on a clinical disease before it becomes clinical. To define a behavioral disease by ignoring behavior. To develop guidelines without providing any guidance.  This has proved to be a surreal policy experiment. Despite the mounting evidence that the Amyloid Cascade hypothesis is incapable of explaining even the most rudimentary of anomalies, there continues to be a willful promotion to maintain the status quo. The handing of the baton to RDoC will continue this status quo, ignoring accumulating anomalies that contradict biological determinism. But the simplicity of a cure inspires a solitary yearning for a panacea, something which RDoC has explicitly embraced with vigor.

Many opportunities exist to address anomalies in research by broadening the study of Alzheimer’s disease to public health. A public health approach to dementias argues that this disease is not only a neurological or chemical disease but that it is also promoted, mediated and/or moderated by other biological, social and psychological conditions and factors. It is time to confront the encroachment of biological determinism in psychology and aim to navigate a way out of this RDoC diagnostic dead-end.

____________

[1] Insel T. (2013) Director’s Blog: Transforming Diagnosis April 29, 2013. Accessed  on 12/8/2015: http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml(link is external)

[2] Nemeroff,  C.B., Weinberger,  D., Rutter,  M.,  et al.  (2013). DSM-5: a collection of psychiatrist views on the changes, controversies, and future directions. BMC Med. 2013;11:202.

[3] Peterson, B.S. (2015)  Editorial: Research Domain Criteria (RDoC): a new psychiatricnosology whose time has not yet come. J Child Psychol Psychiatry. 2015;56(7):719-722.

[4] Weinberger, D.R., Glick, I.D., & Klein, D.F. (2015). Whither Research Domain Criteria (RDoC)?: The Good, the Bad, and the Ugly. JAMA psychiatry, 1161-1162.

[5] Katzman, R., & T. Karasu. 1975. Differential Diagnosis of Dementia. In Neurological and Sensory Disorders in the Elderly, edited by W. Fields, 103- 34. New York: Grune and Stratton.

[6] Katzman, R. (1976). The prevalence and malignancy of Alzheimer disease: a major killer. Archives of Neurology, 33(4), 217-218.

[7] Lijtmaer, H., Fuld, P.A., & Katzman, R. (1976). Prevalence and malignancy of Alzheimer disease. Archives of neurology, 33(4), 304-304.

[8] Tilney, F. (Ed.). (1919). Neurological Bulletin: Clinical Studies of Nervous and Mental Diseases in the Neurological Department of Columbia University (Vol. 2). Paul B. Hoeber.

[9] Hardy, J.A., & Higgins, G.A. (1992). Alzheimer's disease: the amyloid cascade hypothesis. Science. 256(5054):184-5.

© USA Copyrighted 2015 Mario D. Garrett

Excerpt from Garrett M. (2015) Politics of Anguish. Createspace.  

http://www.amazon.com/Politics-Anguish-Alzheimers-disease-century/dp/151...

The Politics of Anguish: How Alzheimer’s disease became the malady of the 21st century.

Since 2015, neurological research surpassed cancer research at the National Institutes of Health (NIH). It was with great hindsight that the National Institute on Aging (NIA) managed to champion a disease that was, for all intents and purposes, a neurological disease—Alzheimer’s disease. The NIA and Alzheimer’s disease have a symbiotic relationship from the beginning of NIA’s conception in 1974. This emphasis meant that the NIH/NIA had to diminish the role of social factors in Alzheimer’s disease research. But how effective has this approach been? The final judgment needs to be based on outcomes, and the NIH/NIA outcomes are starkly devoid of substance both in theoretical development as well as in practical applications.  We still do not know the exact role of the plaques and tangles in the brain, and what knowledge we have we still cannot apply to elevate some of the symptoms let alone cure the disease. After a century of false hopes, it is time to re-evaluate our approach. The constant search for a cure is becoming a worthless meme. Perhaps we can learn something from cancer research.

Cancer research continues to evolve, but one lesson learned, is that cancer is not simple and not one drug will cure all cancers. We need a similarly nuanced understanding of dementias. Why such a simple understanding is not embraced might have something to do with the politics of how research funds are managed. In Alzheimer’s research there is a hierarchy, a cabal, a virtual club whose members receive most of the federal research funds and who determine the agenda. It's a powerful club that determines the direction of research and determines how to frame the disease, how to define it for the public and what is prioritized. But the direction this inner sanctum charted has resulted in a research cul-de-sac. For more than a hundred years we have been encouraged to foster a false hope of a pharmaceutical product, a drug, which will cure Alzheimer’s disease. This has not happened and this will never happen. And the reason why this can be said with such apparent gusto is because we still do not know what we are trying to cure. The construct we now call Alzheimer’s disease is so broad that any intervention that shows any diffuse outcome, will be heralded as a cure. But despite these advertisements, the disease remains elusive. There are numerous researchers who have pointed out anomalies in research, stressing that the direction we are taking is incomplete (Ballenger, 2006).

Sixty years ago David Rothschild highlighted anomalies that he optimistically anticipated will “…open(s) up many fields of study—for example, unfavorable hereditary or constitutional tendencies, and unfavorable personality characteristics or situational stress.” (Rothschild, 1953, p. 293) Unfortunately it did not. The science of Alzheimer’s disease remains firmly and reticently rooted in biology and neurology, despite compelling evidence that this mechanistic approach is too simplistic and does not explain observations. Another physician predicted how future researchers might use the knowledge of plaques and tangles as “…a good playground…” (Perusini, 1911, p. 144). The historical context tells us that researchers today keep ignoring the complex facets of Alzheimer’s disease and playing a game of causality—that biological markers translate to behavior. And we are paying for these choices by being denied any progress towards understanding the disease, or being closer to a cure or alleviating the disease.

Science is not a destination but a journey. It is purely a method of epistemology, of assimilating knowledge. It is not scientific “knowledge,” but knowledge that is gathered using “scientific methods.” All scientific knowledge is incomplete (or wrong), since science continues to generate more detailed questions which determine a better methodology, that result in more complex and accurate results.  As a function of this process, science is based on reviewing all information, assimilating all observations in a model and being able to predict outcomes. Despite all the science invested in studying Alzheimer’s disease, there remain numerous anomalies.  Why these anomalies remain unrecognized is not due to ignorance, nor incompetence, but due to a political strategy--it is intentional. There is a way out of this research cul-de-sac but we have to confront the truth that Alzheimer’s research is politicized to the detriment of humanity.

Except (edited for this blog) from the book: The Politics of Anguish: How Alzheimer’s disease became the malady of the 21st century. Mario Garrett. Createspace.

References

Ballenger, J. F. (2006). Self, senility, and Alzheimer's disease in modern America: A history. JHU Press.

Rothschild D (1953) Senile Psyhcoses and Psychoses with Cerebral Arterioscelrosis p289-331 in Kaplan Oscar J (ed) Mental Disorders in Later Life, 2nd Edition. Chapter XI.

© USA Copyrighted 2015 Mario D. Garrett

How Would Real Capitalism Work?

The derivatives market is twenty times the total world economy. At $1.2 quadrillion the derivatives market is undefinable,  complex, unregulated, and highly profitable. Derivates are a complex set of investments that are based on future events. For example if i bet that someone who has aides is likely to die and then I can buy their life insurance policy earlier and make a profit that is a derivative transaction. But more importantly, if I take action to make sure that that person does not live longer (by restricting life-extending medications, or restricting access to information that might delay their demise, then such action would increase the value of my derivatives.  Then there are those in the "positive feedback loop" that just follow the market as it goes down and sell a proportion of their stock and hopefully countered by "negative feedback loop" that buy when it goes low and sell when high.