Saturday, January 30, 2016

A New Paradigm for Alzheimer's Disease Research

From lost hope emerges a new perspective. After more than a century of research into Alzheimer’s disease we have reached a research cul-de-sac. By eradicating the plaques and tangles from the brain, a series of studies reported that the disease worsened 1,2. What this tells us is that the disease is more complex then just a build up of mis-folded proteins.
In panic, the National Institute on Aging, coopting the Alzheimer’s Association, published new guidelines for Alzheimer’s disease in 2011. These guidelines effectively transformed a clinical disease--a disease defined by its behavioral manifestations—into a pre-clinical disease. What this means is that now Alzheimer’s disease exists before there are any clinical manifestations. This seems counterintuitive given the studies showing that drugs that cleared the plaques and tangles—the only pre-clinical indicators--did not cure Alzheimer’s disease and in fact made it worse. But the increasing power of the pharmacological industry in establishing the research agenda seems limitless. Now pharmaceutical companies can experiment with patients before they even start showing symptoms of the disease. In effect, curing a disease before the clinical disease emerges. But so far, they have had little success. 
Since the early 1990 pharmaceutical companies have been attempting to halt early onset dementia among an unfortunate community in Medellin, Colombia. Discovered by Francisco Lopera in 1984, this heritable variant of Alzheimer's disease share a common ancestor—a 16th-century Spanish colonist who to this day has infected 5,000 patients in 25 families. 4  The reason why this approach—trying to find a biological cause of the disease--has been so resilient despite mounting evidence contradicting this approach, is that there has not been a competing theory to challenge it. Until now.
A crescendo of mounting criticism has established that Alzheimer’s disease is more complex than a cascade of misfolded proteins. That even though people might have the plaques and tangles, some do not express the disease, while some who express Alzheimer’s disease have been shown to have no significant plaques and tangles. In addition, with older adults, multiple studies have shown that the correlation between plaques and tangles and Alzheimer’s disease declines with age. One way to explain these anomalies is to broaden the study of Alzheimer’s disease. One such approach is to see it as a public health disease. 5
A public health perspective argues that there are multiple traumas to the brain. Some of these can be a virus or bacteria, while some are physical (like a concussion). We are seeing more and more how physical trauma causes dementia among NFL football players. But sometimes this trauma is managed and contained. A good example of this process is looking at stroke victims where we see more than 30 percent improving. In such cases, the penumbra—the protective cells that surround the initial trauma—is contained and the death of cells remains localized. Two factors promote this healthy brain. One is blood supply—Perfusion, while the other is growing your brain--Plasticity.
Perfusion allows for the brain to receive adequate nutrients and energy to heal itself. Having a healthy brain improves the chances that a trauma is contained. Plasticity on the other hand ensures that there is enough flexibility in the brain that if the brain needs to contain an area that other parts can take over that lost function. Without these two factors the penumbra will continue to grow and affect larger areas of the brain—and such damage will go beyond plaques and tangles. This broader public health interpretation of Alzheimer’s disease assimilates both the traditional Amyloid Cascade hypothesis and explains the increasing number of studies showing how external factor influence the incidence of Alzheimer’s disease.
The beauty of this public health approach is that we do not have to wait another hundred years before we realize that we are in a research cul-de-sac. We can start implementing programs that reduce and lower the exposure to traumas. Reduction of concussions (in sport, military, recreational activities) should be made a priority. Programs that educate on the effects of smoking and heavy drinking on the brain need to be promoted, as well as programs that address environmental toxicity both in the air and in our water. For perfusion, city walkability programs, and social engagement programs all promote walking, swimming, light exercise, gardening among other activities. While improving plasticity involves social activities, dancing, music and other cognitive exercises.
Pharmaceutical influence can determine federal research policy, but with knowledge, individuals can protect themselves and their family from exposure to this deadly disease that we still do not fully understand.

A version of this article can be found in:
A complete story of this blog can be found in my recently published book: 

References.
1. Gilman S., Koller M., Black R.S., Jenkins L., Griffith S.G., Fox N.C, et al. (2005). Clinical effects of Abeta immunization (AN1792) in patients with AD in an interrupted trial. Neurology, 64:1553–62.
2. Boche D., Donald J., Love S., Harris S., Neal J.W., Holmes C., et al. (2010). Reduction of aggregated tau in neuronal processes but not in the cell bodies after Abeta42 immunisation in Alzheimer’s disease. Acta Neuropathol, 120: 13–20.
3. Jack  C.R., Albert M.S., Knopman D.S., McKhann G.M. Sperling R.A., Carrillo M.C., ... & Phelps C. H. (2011). Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia, 7(3):257-262.
4. Lopera F., Ardilla A., Martínez A., Madrigal L., Arango-Viana J.C., Lemere C.A., ... & Kosik K.S. (1997). Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation. Jama, 277(10): 793-799.
5. Garrett MD & Valle R (2015) A New Public Health Paradigm for Alzheimer’s Disease Research. SOJ Neurol 2(1), 1-9. Page 2 of 9
© USA Copyrighted 2016 Mario D. Garrett

Monday, January 25, 2016

Bad Drugs and Older Adults

Bad Drugs
Drugs work in mysterious ways. Sometimes it benefits us, other times there are negative side effects. Half of all adverse drug occurs when five or more drugs are taken and it is nearly certain that there will be a reaction when eight or more drugs are taken. How dangerous can this be?
Sometimes prescribed medications cause death. The Danish physician and researcher Peter C. Gøtzsche from the Nordic Cochrane Centre, estimates there are 15 times more suicides among people taking antidepressants than are reported by the US Food and Drug Administration. By looking at Danish prescription statistics for antipsychotics, benzodiazepines, and antidepressants he estimated that the death rate for older adults was between 1 and 2 percent.  Based on these Danish death rates he estimates that for the U.S. and European Union combined an estimated 539,000 older adults die from these drugs every year. But it is not just antidepressants that can cause adverse reaction. Especially with older adults because our metabolism changes with age, the filtering of the drug in our bodies is compromised and becomes less efficient. Drugs remain in our blood longer. As a result, the effect of drugs changes as we age.
Every few years the American Geriatrics Society (AGS) releases an updated and expanded Beers Criteria (after the originator of the first list, Mark Beers)--a list of potentially inappropriate medications for older adults that is developed from reviewing over 6,700 clinical studies. The report is complex, technical and detailed and needs to be reviewed with your physician. However, as a summary, it is important to realize how common bad side effects are for most of the medications that we take.
For example, among patients aged 65 years and older, insulin or warfarin (Coumadin®) was the cause of one in every three drug reactions that resulted in an emergency hospital visit and was responsible for nearly half of all drug reaction hospitalizations. Analgesics for chronic pain cause slowed breathing and caused constipation. NSAIDs, such as ibuprofen (Advil®) and naproxen (Aleve®), are generally not recommended for the older adults because of stomach and intestine irritation and possibly raising blood pressure. While acetaminophen (Tylenol®) increased the risk of hypertension by a third. Some medication prescribed for schizophrenia and bipolar disorder Aripiprazole (Abilify®), clozapine (Clozaril®), and risperidone (Risperdal®) may increase blood sugar indirectly due to weight gain. Decongestants and other anticholinergics that we can get at the pharmacy without a prescription can cause confusion, urinary retention and other problems. For example Pseudoephedrine (Sudafed®) can raise blood pressure. Researchers found that half of all older adults taking anticholinergics showed mental decline.  Beta-blockers like Atenolol (Tenormin®), sotalol (Betapace®) prescribed for hypertension, arrhythmias, and thiazide diuretics, such as chlorothiazide (Diuril®) and indapamide (Lozol®) prescribed for hypertension and congestive heart failure can increase the risk of diabetes. Corticosteroids such as prednisone and methylprednisone (Medrol®) prescribed for arthritis or asthma increase blood sugar and can lead to type 2 diabetes. Erectile dysfunction medications like sildenafil (Viagra®), tadalafil (Cialis®) and other medications may cause visual and hearing disturbances. The biggest category of drugs taken by older adult  is statins for cholesterol, where atorvastatin (Lipitor®), simvastatin (Zocor®) and other statins may create very low levels of cholesterol that may lead to depression, memory loss and confusion. Some statins may cause liver damage. Congestive heart failure medications such as digoxin (Lanoxin®) and diuretics are at risk for electrolyte imbalances and therefore risk poisoning the body through increased toxicity. Hip fracture is increased among elderly patients who take proton pump inhibitors such as lansoprazole (Prevacid®), esomeprazole (Nexium®) and omeprazole (Prilosec®) and to a lesser extent by H2-blockers such as cimetidine (Tagamet®) and famotidine (Pepcid®).
Because so many medications are excreted via the kidney, it is important for elderly patients to have kidney function assessed regularly. Impaired kidney function may require adjustment of medication dosages. What we eat can also influence how these drugs react in our body. Certain drugs have dietary implications, including foods to avoid and nutrients that are essential. Some medications should be taken on an empty stomach, some with food.
Older adults also use drugs that they buy from the dispensary without getting a doctor’s prescription. These “over-the-counter” drugs are readily available, and people again feel that they are safe. Nearly half of prescription users also take at least one over-the-counter medication. In addition, there is an increased use of herbal or dietary supplements (eg, ginseng, ginkgo biloba extract, and glucosamine) by older adults. Almost three-quarters of older adults use at least one prescription drug and one dietary supplement. Sometimes we do not tell our doctor that we are taking these supplements because we think they are not important. But herbal medicines may interact with prescription drugs and lead to adverse events. Such adverse events as when ginkgo biloba extract is taken with warfarin, causing an increased risk of bleeding, or when St. John's wort is taken with serotonin-reuptake inhibitors, increasing the risk of too much serotonin causing symptoms ranging from mild (shivering and diarrhea) to severe (muscle rigidity, fever and seizures). Severe serotonin syndrome can be fatal if not treated. A study of the use of 22 supplements found potential interactions between supplements and medications in half of these supplements.
We do not know all of the ill effects of medications on older adults, especially among older women, because these drugs are rarely, if ever, tested among older adults. The drug-drug interactions, side-effects, cost of medications, medications that should have been stopped ages ago, and medications that are inappropriate for older adults suggest that the fewer drugs you take the safer you are. Some people cannot reduce their medications, but by discussing your medications with your physician, you can start the discussion to try and reduce and possibly eliminate some of your drugs. In some cases replacing medication with other treatments, such as psychotherapy, exercise, social activities or some behavior modification training might be worth exploring especially for behavioral concerns. For some who have found a balance, their medication regime is sustaining life. But it seems that there are many others who are struggling to find this balance.

© USA Copyrighted 2016 Mario D. Garrett


Tuesday, December 22, 2015

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

Tuesday, December 15, 2015

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.

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.