Personalized Medicine

In my previous 2 posts, I have begun to flesh out my concerns with population-based medicine. In this post, I want to (1) elaborate further on the problem of population-based averaging (which forms the basis for epidemiology & randomized-controlled trials), and (2) address the nouveau idea of “personalized medicine.”

Population-based medicine originated with studies of infectious diseases in the late 1800s, and John Snow’s analysis of cholera in Great Britain is a shining example of how the study of a disease process can allow us to hone in on the causative agent (in this case, a dirty water pump). In his studies – and in those that followed him – disease incidence was averaged  across general parameters, like geography (e.g. streets, cities, zip codes). At first, his statistics were met with resistance, as physicians (like me) argued that there was too much individual variation to arrive at any useful conclusion. However, he and others showed that when the etiology could be traced back to a single cause – e.g. a bacteria, a water pump, etc. – population-based averaging worked extremely well since the causative factor often affected/infected anyone, regardless of age, gender, race, or creed (or any other personal characteristic you can imagine).

Fast forward a century, when our leading biological killers – heart disease & cancer – are not single agent mechanisms: there is no single gene, exercise regimen, or food article that uniformly causes these diseases (Ref. 1, 2). In fact, it is precisely our individual-to-individual variation that puts us at risk for these diseases. The lack of a single causative agent is manifested in the size of randomized controlled trials evaluating the efficacy of cholesterol-lowering drugs (Ref. 3): unexplained and pronounced variance in the population requires thousands of individuals (i.e. samples) to tease out the effect of any single factor, like a drug. In population-based averaging, you cannot know what you are “averaging out.” We average disease incidence or intervention outcomes over a group of people amalgamated by age, gender, and race, but imagine the number of personal characteristics that are unaccounted for, which may be as bizarre as eye color, skin pigmentation, or dental crowding (see footnote); these variables are the ones being smoothed over. There are innumerable hidden (i.e. latent) variables beyond our grasp, factors that can subtly or dramatically affect your disease risk. An example I can imagine: people who eat a Big Mac >1/week are at an increased risk for coronary artery disease, but people who eat a Big Mac and run 3 miles/day have a decreased risk for coronary artery disease. (This is a real example. I had a 50 year old patient who, in spite of eating McDonald’s every day, had perfect cholesterol and a heart that beat every other second. He has also run 4-5 miles every morning for the past 20 years)

So this leads you to think, “Well, why don’t we improve our stratification of people? Let’s just lump people together by X and Y, instead of X alone.” Though it sounds simple, we have no grasp over the number of features sufficient to define a group. On one hand, we have been averaging people for the past century by race, gender, and age (easily identifiable features). On the other hand, genotyping technology is now allowing us to measured tens of thousands of molecules at once, and the goal of projects like the $1000 genome or of companies like 23andme is precisely that: take thousands of measurements and calculate risk factors. The problem, however, has not been ameliorated, as we are leagues away from understanding how the genome translates into the phenome, and, consequently, the gross, unpredictable characteristics like BMI or lifestyle still need to factored into the risk prediction scheme.

Apart from the gargantuan issues underlying clinical genomics (Ref. 4), we are still plagued by the issue of combinatorial risk, as I alluded to earlier in thinking about Big Macs and running: risk factors are analyzed independent of each other, but it is hard to predict how these variables operate in tandem. When 23andme.com calculates your risk for a disease, it is based on the presence of a particular allele of a gene, often identified through genome-wide association studies (GWAS) that look for DNA polymorphisms correlated with disease incidence. So, if a single feature (a polymorphism, i.e. an allele) is correlated with disease in the population, they say that it increases (or decreases) your risk for the disease. What we cannot predict, however, is how various alleles blend together in an individual’s genetic soup. If you have a particular allele in MHC and/or IL12B (major histocompatibility complex and interleukin 12b, respectively; two genes with important roles in immunity), you may have an increased risk for psoriasis, a disease associated with an overactive immune system (Ref. 5). What happens, however, if you also possess the allele that predisposes you to tuberculosis (Ref. 6), an infectious disease that can slip below the radar of your immune system? Genetic screens would report your independent risks for these 2 diseases, yet these two diseases would be dependent entities inside of you by their very coexistence in your body. Would your revved up immune system (from your predisposition to psoriasis) abrogate your susceptibility to tuberculosis, or are they operating through independent pathways? Perhaps, unpredictably, they synergize with each other to produce a tertiary disease in a separate part of the body.

Breast cancer genetics is another story of unpredictable combinatorics, where the predisposition to cancer imparted by mutations in BRCA1 & BRCA2 is modulated by lfestyle factors and other genes. Bearing children, for instance, is associated with a decreased risk for breast cancer (Ref. 7), while variations in the AIB1gene seem to increase risk (Ref. 8). Yet, we were unaware of these risk factors when  breast cancer was first studied, and the picture of disease risk resembled the blue curve in the illustration below. Having teased out a few of the hidden variables, like parity or AIB1 status, we can better deconstruct risk by identifying subgroups (the orange and red curves) within the larger population.

Whether the hidden variable is the number of Big Macs you eat or the type of mutation in your genotype, the number of possible risk factors is astronomical. Currently, our picture of most complex illnesses resembles the graph above: we started with the blue curve, and, now, we have deconvolved it into a few smaller curves (orange & pink), but how many subpopulations exist within these? What variables are we inadvertently averaging out when we assemble these population-based risk assessments?

This is not personalized medicine. This is stratified medicine (Ref. 9), and the substratification of people is a interminable task due to its fractal-like complexity.

Footnote:

Although these features may seem like bizarre clinical variables in allopathic medicine, such phenotypic characteristics form the foundation of diagnosis and treatment in Ayurvedic medicine.

References:

1. Bogardus, C. Missing Heritability and GWAS Utility. Obesity. 17 (2), 2009.
2. Kraft P, Hunter DJ. Genetic risk prediction—Are we there yet? N Engl J Med 360:1701–1703 (2009)
3. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of Cholesterol Lowering with Simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet. 2002 Jul 6;360(9326):7-22.
4. Ng, P.C., et al. An Agenda for Personalized Medicine. Nature (461), 2009
5. Zhang, X., et al. Psoriasis GWAS Identifies Suceptibility Variants Within LCE Gene Cluster at 1q21. Nature Genetics, 41 (2009).
6. Thye, T., et al. GWAS Identifies a Susceptibility Locus for Tuberculosis on Chromosome 18q11.2. Nature Genetics, 42 (2010).
7. MacMahon, B., et al. Age at first birth and breast cancer risk. Bulletin of the World Health Organization, 43 (1970).
8. Rebbeck, T.R., et al. Modification of BRCA1 and BRCA2 associated Breast Cancer RIsk by AIB1 Genotype and Reproductive History. Cancer Research, 61 (2001).
9. Trusheim, M.R., et al. Stratified Medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nature Reviews Drug Discovery, 6 (2007).

A Service Industry

I still remember my pediatrician who saw me for my earache when I was 8 years old. I remember his laugh, and how reverant my mother seemed of him. As patients, we place a monumental amount of significance on our physicians. Now that I’m on the other end of things, it feels unethical not to reciprocate the value placed on us by patients. While such sentiments are often voiced at our white coat ceremonies and commencements, this style of practice is not the norm.

I realize that I am uncomfortable viewing medicine strictly as a service industry. When the bottom line is control of population-level parameters and efficiency of the system, we are trained to see only the similarities in people, lumping them into classes and categories. What bothers me is that our patients are not distorting and stereotyping us accordingly. How can I view someone as simply part of the herd, when he or she views me as someone memorable?

Lose the forest for the disease: Part II

Epidemiology has encroached on our perception of multifactorial diseases (see previous blog entry), and it has also distorted the way in which medicine is practiced and taught. Now that the medical system is encumbered with complex diseases – heart disease, diabetes, obesity, etc. – we are attempting to identify causative factors for these expensive illnesses. Yet, whether we examine coarse characteristics like race or fine-grained genetic features, the best we can do is to identify risk factors, not causes. Risk factors – like age, gender, or genetic mutations – are identified based on population-level trends in disease incidence. The use of the phrase “risk factors” originated in The Framingham Heart Study (1948), an epidemiologic study designed to examine how cardiovascular disease is linked to lifestyle, and, based on the 5,000 or so men and women studied in Framingham, MA, they have published a widely used risk calculator. Can anyone’s risk of developing cardiovascular disease be boiled down to these 7 characteristics? If you live in Framingham, MA, it can, but if you live anywhere else (e.g. the UK, Ref. 1), your risk may be significantly different. This is the first problem that arises with the use of epidemiology to make claims about disease: how similar are you – or your patient – to the individuals followed in a particular study? Are broad categories like age, gender, and race sufficient to constitute similarity? (Given the study in the UK, probably not)

While this suggests that we should be designing more nuanced studies – where we assemble cohorts of patients that resemble real and varied communities (e.g. African Americans in Cleveland, or 2nd generation children of Jewish immigrants in NY) – current research, particularly in genomics (Ref. 2), is moving in the opposite direction, amassing thousands of individuals grouped together by coarse features like race, gender, or age. The impressive size of these studies effectively allows us to study how a handful of features vary with disease, and the astronomical number of individuals is necessary to wash-out the large amount of individual-to-individual variation that confounds complex diseases. The high degree of heterogeneity in the study population weakens the correlation to disease incidence, making the widespread application of the calculated risks tenuous. Often, the few genetic polymorphisms associated with a disease can only explain a fraction of the disease incidence (Refs. 3,4). For instance, variation in two well-publicized genes linked to obesity accounts for less than 2% of the variation in BMI (Ref. 3). The individual variation seen with complex illnesses – from the severity/features of the illness, to the lifestyle surrounding it – and the large numbers of people required for research studies suggests that there may be underlying latent variables or population stratification (read about Simpson’s paradox), and identifying the latent variables may paint a more nuanced picture of the factors that influence a person’s disease risk.

Another issue lies in bringing epidemiology to the clinic, for it influences how we view our patients. Translating epidemiology into the practice of medicine begins in medical school, and the influence of epidemiology is apparent from the problem cases that comprise our board exams. These cases often sound like “a 44 year old African American male with a history of diabetes and hypertension presents with complaints of chest pain…”, where every word clues us in to their risk factors: age, gender, race, diabetes, and hypertension. As a result, medical students are trained to view patients as nothing more than a grocery list of risks – a dehumanizing process (for both us and the patients) that may have limited utility. For both the patient and physician, the care of health operates at the level of the individual, not the population. Rather than understanding what is “normal” for the patient in front of us, we are trained to define “normal” relative to the population. Ideas like population-based normality and risk factors are tools for regulating public health and, as such, serve as a wonderful resources for institutions – the CDC, the WHO, governments, cities, etc. – because they can use these population-based averages to implement large-scale health interventions. If, for example, African Americans living in a particular zip code have higher rates of colon cancer and diabetes, the city government might team up with civil engineers and city planners to reduce the number of fast food chains and increase the number of grocery stores (read more on food deserts).

There is no doubt that epidemiology continues to improve our understanding of health and disease. Indeed, the health of entire countries has benefited from the lessons learned from Framingham (Ref. 5). Nonetheless, we have to be cautious in (1) making the ecological fallacy in assuming that an individual behaves like the average and (2) viewing patients as nothing more than an inventory of risk factors.

References:

1. Brindle, P. Predictive Accuracy in the Framingham coronary risk score in British men: prospective cohort study. British Medical Journal, 327 (2003).
2. Ahmed, S. Newly Discovered Breast Cancer Susceptibility Loci on 3p24 and 17q23.2. Nature Genetics, 41 (2009).
3. Bogardus, C. Missing Heritability and GWAS Utility. Obesity. 17 (2), 2009.
4. Kraft P, Hunter DJ. Genetic risk prediction—Are we there yet? N Engl J Med 360:1701–1703 (2009)
5.  Puska, P. From Framingham to North Karelia: From Descriptive Epidemiology to Public Health Action. Progress in Cardiovascular Disease, 53 (2010).

Diet and Epidemiology: Colon Cancer

Over the past century, as our medical system has grown, I think the influence – both positive and negative – of epidemiology on medicine has also become more pronounced. Epidemiology has always lurked below the surface of medicine – from ancient Indian physicians recognizing that boiled water reduced the incidence of infectious disease, to Dr. John Snow tracing patterns of infection to identify the source of a cholera outbreak in 1854 – and has allowed for sweeping public health interventions that have drastically reduced morbidity and mortality (e.g. smoking cessation). In fact, epidemiology is inseparable from the practice of medicine, as a physician ought to take into account the patient’s race, where he/she lives, what he/she does, etc. because all these factors have been shown to predispose individuals to a variety of diseases.

This model for viewing individuals works well when we are dealing with a single causative agent (e.g. E. Coli from beef); we can sort through patients’ histories and symptoms to find the 1-2 factors (e.g. eating at Jack-In-the-Box) that correlate most strongly with the chance of being infected and, then, treat the patient accordingly, for these 1-2 factors can explain a majority of your risk. After the antibiotic era (which emerged in the 1930s, following the discovery of penicillin) knocked infectious diseases off the list of primary killers (see table below), American began to turn its attention to the diseases that were left: heart disease, cancer, diabetes, et al. (as of 2009, Ref. 1).

Yet, when we apply epidemiologic principles to these multifactorial diseases, we rarely identify a  characteristic of the patient that can explain the majority of their risk. An increased risk for colon cancer, for instance, has been linked to diets high in saturated fat and red meat, with the increased risk estimated to be as high as two-fold. However, it’s difficult to take into account all the other factors that influence cancer risk: the vegetables & fruits you eat; how those veggies are prepared; the amount of exercise you do; what kind of exercise; where you live; how much meat you eat; how your meat is prepared. So, one the first studies (Ref. 2)  – now cited nearly a thousand times – that pointed out the dietary association found that chicken-n-fish seems to be protective (decreased risk of colon cancer) compared to red meat-heavy diets. Yet, they only corrected for total energy intake in their model; they plugged vitamin intake, exercise, and fiber into their model one at a time and found that these factors were not influential. With complex phenomena, it’s no surprise that individual factors – especially biochemical modulators like vitamins – have no “significant” effect in isolation. (I would have liked to see what the combinatorial effect of vitamins, fiber, and exercise was.) Jump ahead 20 years and the situation hasn’t improved much: a group (Ref. 3) recently tried to tease apart how cooking methods – rare vs. well-done – affected colon cancer risk, and I’ve shown their Table 2 below. You can see that fruit intake, vegetable consumption, and physical activity all trend inversely with red meat intake. Once again, they found that each of these confounders does not influence colon cancer risk when examined one by one… so, they ignored all of these lifestyle modifiers and told us, again, that red meat increases your risk of colon cancer. (Obviously, grilled meat is more highly correlated)

Is red meat correlated with an increased risk of colon cancer? Sure. But does it cause colon cancer? Hard to say. In these studies which form the backbone for the no-red-meat argument, there are so many confounding variables that it is nearly impossible to attribute the observed effect to any single player. In the end, what these studies indicate is that it’s not red meat alone, but, rather, the myriad factors contributing to a red meat-heavy lifestyle that result in an increased risk for colon cancer. In fact, I think that the dietary link may have more to do with the way food is prepared rather than the food items themselves. For instance, a 6 oz. serving of grass-fed beef paired with an equal (by volume) serving of kale & carrots would (by my estimates) be less harmful than a 6 oz. slab of corn-fed beef paired with an iceberg lettuce salad, a cup of salty soup, a dinner roll, a baked potato, and a few tablespoons of green beans. In light of such dramatic variations in food preparation, we need to think about the cultural & societal norms that lead to such tumorigenic and obesogenic dietary patterns, for this is where the problem lies.

Cutting out red meat from your diet is not the same as not drinking water from a cholera-infected fountain: there is no single player, and simply removing red meat won’t eliminate the risk for colon cancer. Yet, with such few medical breakthroughs nowadays, biomedical research and the popular press leap upon whatever marginal gain they may find. In the end, these kinds of results give the media something to shout about for a time being (e.g. resveratrol, lycopene, ad infinitum) before the next food article demonstrating incremental benefits is “discovered.”

1. Kochanek, K.D., et al. Deaths: Preliminary Data for 2009. National Vital Statistics Report, v. 59, n. 4. March 2011.

2. Willett, W.C., et al. Relation of Meat, Fat, and Fiber Intake to the Risk of Colon Cancer in a Prospective Study of Women. New England Journal of Medicine, v. 323 (24), 1990.

3. Cross, C.J., et al. A Large Prospective Study of Meat Consumption and Colorectal Cancer Risk: An Investigation of Potential Mechanisms Underlying this Association. Cancer Research, v. 70 (6), 2011.

Lose the forest for the disease: Part I

I finally started 3rd year of medical school – where we rotate through the various branches of medicine for 1-2 months at a time – and I am continually struck by the formulaic approach to health that seems ingrained in both the practice and training of medicine. Medicine, as it is currently taught, centers around the development of a “differential diagnosis” – a list of all possible diagnoses based on the patient’s symptoms – and the consequent treatment plan. To develop this mental workflow, we don’t pour over monolithic medical treatises. Instead, we resort to review books (of the BluePrints or Board Review Series variety), which distill man’s current knowledge of the human body into easily digestible factoids of the form “ABC Disease is characterized by (insert rash here) and (insert cardiac abnormality here), commonly found among (insert ethnicity, gender, and/or age group here). It is diagnosed by (insert 2-3 symptoms and laboratory test here). First line of treatment is (insert medication here); second line of treatment is (insert surgical intervention here).”

It is mindnumbing. Worse still, I suspect that this educational paradigm inhibits patient-centered healthcare.

You can imagine how the average medical student approaches a patient: nervous, yet eager, she hangs back while her attending physician exams the patient. Suddenly, the physician lifts up the patient’s leg, and asks the student, “What is unilateral swelling of the leg associated with?” The student stares blankly at this leg, which is now conceptually dismembered from the rest of the patient, grasping for shreds of a differential diagnosis. In the end, she manages to recall a few catch phrases from her review book (lymphadenopathy, DVT, etc.), and her attending fills in the rest.

Now, this model for education is arguably necessary, for, when we enter a patient’s room, we need to rapidly sort and file all the data that we collect, and, more importantly, we need to know what we are looking for. The problem is that, more often than not, we lose the patient for the disease. Instead of interacting with the patient as we would our uncle or our neighbor, we interact with them as a bag of possible diseases. We have all experienced this kind of medical care (for it is the most common): the doctor comes in, asks you how you’re doing, ignores your response, asks you a few questions, listens to your heart, and – poof – diagnosis made, pills prescribed, medicine done. This sounds a bit extreme, but I have seen variants of it already – and I’m only 2 months into my medical school rotations. Recently, I worked with a pediatric gastroenterology fellow whose patient interview looked more like canned, well-rehearsed acting than genuine human interaction. She would ask her patients how they felt, and then, while avoiding eye contact, drop in the timely “Hmm” or “I see” for reassurance. After blatantly ignoring every complaint they had, after ignoring the rash covering their body head to toe, after ignoring their attempts at conversation, she would then procede to ask about belly pain… and nausea, and vomiting, and bloody stools, etc., effectively turning this person into a grabbag of gastrointestinal symptoms. After going fishing for GI complaints, she would then pull out one of her GI tools, be it an endoscopy or a CT scan or perhaps a recommendation to eat more fiber.

This GI fellow never rose above her formulaic medical training. She memorized her board review textbooks, and, along the way, she also memorized the patterns of speech – the Hmm’s and I-see’s – characteristic of her revered teachers. To her, each patient was merely a variant of the same gastrointestinal grabbag. Her attending physician, however, seemed to pay more attention to what the patient said, taking into account what they ate, how they felt, where they’ve been, etc., commingling his intel with light-hearted conversation. The seemingly disparate bits of information buried in genuine conversation have their own place is assessing a patient’s health, and the value of this kind of interaction seems to become more appreciable with experience (as older physicians tend to listen- or are perceived as listening - to their patients more).

A corollary to “Cancer and the Immune System”

My friend sent me some musings (from Whole Health Source) on how our immune system is able to control cancer in our body.

At our recent MSTP summer retreat, we heard from Dr. Robert Darnell, MD, PhD, who is doing research on paraneoplastic syndromes (i.e. diseases that occur in conjunction with neoplasms/cancers). He focused on a set of neurological diseases, in which an individual experiences a sudden and rapid (<1 week) decline in cerebellar function. They wake up one day feeling a little dizzy. Within a day or two, they can barely stand, they have tremors & muscle spasms, loss of memory, et al., so they go see a neurologist. It turns out that the underlying cause is autoimmune dysfunction. These people actually have a small cancer somewhere else in their body that their immune system has kept under control for years. It is well-known that everyone develops cancers naturally; some people are just better at fighting them or controlling them than others. At some point, the cancer (in what seems to be a stochastic process, with our current understanding) starts producing a set of proteins that are only found in the cerebellum – a part of the body which the immune
system does not normally see. So these patients start producing antibodies to this “foreign” protein, and, if these antibodies penetrate the blood-brain barrier (which also does not happen very often), then the patient’s immune system starts attacking the cerebellum.

It’s this kind of research that makes me wonder what my genetic background is capable of. My dad has psoriatic arthritis (a genetically-linked autoimmune disease; a combination of psoriasis and arthritis), so there is a chance that I have those genes. But, even if I do, my genetic background (i.e. everything else in my genome) will affect how/when/if this disease manifests itself.

The immune system is one of the most poorly understood systems in human clinical biology, and yet it appears in every subspecialty of medicine. There are very few treatments that robustly improve immune
function. When someone has a skin disease (e.g. psoriasis or eczema), or asthma, or joint pain – all of which have an autoimmune basis – we just shoot them up with steroids, which provide temporary relief and
unacceptable side effects.
My dad found that his psoriatic arthritis can be controlled in 2 ways: infliximab and flax seed fiber. Infliximab is an antibody that binds, and thereby decreases, TNF-alpha – a key molecule that is secreted in
inflammation (and inflammation is a manifestation of an immune system flare up). He found that infliximab can control his disease for a few months, and then he starts getting flare ups (rashes, joint pain) again. At that point, he switches to hefty doses of flax seed fiber: a few tablespoons of freshly ground flax seeds every morning. Flax seeds are an incredible source of 18-carbon polyunsaturated fatty acids (specifically, a high ratio of omega 3s relative to omega 6s). Fatty acids – both saturated and unsaturated – are the substrates for several families of inflammatory signalling molecules, but the body has a harder time converting omega 3′s into inflammatory molecules (it converts saturated fats and omega 6′s relatively well). Consequently, keeping your adipose tissue well-stocked with omega-3 fatty acids can bring about an anti-inflammatory physiological state. Salmon also has a hefty dose of omega 3′s relative to omega 6′s (I think the ratio is even higher than flax seeds, but today’s fish also carry heavy metals and toxins). Omega 3′s are found in sprinklings throughout nature: whole grains (oats, wheat), nuts (almonds), olive oil, seeds (kiwi fruit seeds), milk fat, etc. Given the average American diet (bleached/processed grains; protein & saturated fat from land animals; highly processed dairy products), I think it’s no surprise that this country is a hotbed of autoimmune dysfunction.

Motivation for this blog

This is an excerpt from an email I sent to my friends, who then suggested blogging my frustration:

Before I actually came to medical school, I used to think that medicine & science were very noble pursuits. But now I’m here and it seems so mundane and trivial. The public perception of science/medicine is very different from their actual practice. I’ve had the same thoughts about science/medicine since college.
Medical school and graduate school haven’t changed them, and I don’t think getting a PhD is going to change them either.

I spend my days studying seemingly relevant problems (obesity in mice), but – for most of science – the end results are usually miniscule, just a drop in the ocean. And I wouldn’t mind that aspect so much if I actually thought I was doing some good with my time, with my efforts. But I’ve had this nagging feeling since I started medical school (3 years ago) that the problems I am – or will be – addressing
simply are not that relevant….
As a physician, I will spend my time helping people feel better. When I applied to medical school, I had hoped to treat people in a particular way, developing therapies to address the underlying causes
- not simply treating the symptoms. But now I’m here, and I realize how much I am at odds with the system. Biomedical science – and the practice of medicine that springs from it – is rooted in reductionism,
and I don’t think it’s getting us anywhere. Sure, it’s given us vaccines and penicillin, but I think it has also reduced our awareness of complex interactions (namely between people and their environment).
One issue that illustrates the point – and the one that I care the most about – is food. Scientists and physicians have spent the last century whittling down the list of dietary necessities to a list of
vitamins and minerals, and then industry took these key ingredients necessary for life and started manufacturing foods. Half a century later, you have generations of people plagued by diseases of excess
(obesity, heart disease, diabetes).

Here’s another one: all those antibiotics that were developed in the past century have led to a decrease in infant mortality and an extension of life span. People were less likely to die of TB or pneumonia. Antibiotics became commonplace. They are still prescribed haphazardly for colds, stomach aches, etc. when there may not even be a bacterium to kill. Half a century later, you have generations of people suffering from autoimmune diseases (asthma, eczema, allergies, ad infinitum). One theory behind autoimmune diseases is that, during your development, your immune system was not exposed to enough foreign antigens, leading to an inability to distinguish between “self” and “nonself” tissues. So when you’re exposed to a mild irritant, you get a wide-spread flare up of some sort (rash, inflammation). And medicine doesn’t know what the hell to do about these disease: they just give you a f-ing shot of steroids. Eventually, science figures out where it screwed up (though it never admits it). It’s only now beginning to realize it’s shortcomings. It’s finding out that those basic list of vitamins & minerals are needed to live, but, to live WELL, you need alot more. There’s been a surge in the past decade of research on phytochemicals – plant chemicals – that benefit humans. Lycopene in tomatoes (for prostate cancer), lutein in green leafy vegetables (for your eyes), and all those compounds in green tea, soy, and turmeric that slow aging, prevent cancer, and make you lose weight. Unfortunately, the media construes this research in the simplest way: they keep publishing “updated” lists of foods you should eat.
And there are all sorts of things that we are currently doing that have had little forethought. For example, in vitro fertilization bothers me because (1) the people who get it are wealthy, (2) they have defective eggs/sperm/reproductive organs, and (3) they are contributing unnecessarily to overpopulation. The fact that science only caters to the rich and to the developed countries of the world bothers me…… it’s a big part of the reason why I feel that like I’m wasting my time. In regards to (2), recent research is finding that IVF leads to an increased risk of birth defects. We don’t have the data (yet) to examine what the long-term consequences to the gene pool are (due to IVF)….. my budding geneticist instincts tell me we are weakening our reproductive fitness.
As for (3), science/research caters to our selfish desires. The National Institute of Health drives research in this country, so that means science is funded by tax dollars, which means it caters to our personal interests. People want babies that look like them, that have their genes (regardless of the fact that we’ve exceeded the carrying capacity for the earth). So, money is given to scientists to develop In Vitro Fertilization. If people have heart disease because they have poor diet & exercise habits, then money is given to scientists to find “cures” for this “disease.” However, the disease is a consequence of your actions. If the pharmaceutical industry now comes in with a pill to fix it, then you are relieved of your responsibility, of your accountability. Then we, as a society, further fail to grasp the concept of cause-and-effect…… we then exercise even less self-discipline and less forethought, ultimately leading to the downfall of our civilization.
And THAT is how I feel about what I do.

Everything I see is a testament to how little we know, and how much faith we put in our limited knowledge. It bugs the crap out of me. I can’t help but feel that doctors and scientists are ruining the world with their arrogant belief in what they think they know. So I still feel lost…… and I don’t know what good I will do,
though I desperately feel that I need to do something.

There’s also the thought that I should just jump through the hoops, become a rich doctor, and then use my exorbitant income to fund projects/organizations that I believe in. But I don’t think I would be happy living vicariously….