Underlying Causes of the Increase in Neurological Disorders
Billions have been spent trying to discover pharmaceutical treatments for dementia and mental illness. However, the effort has been a near-total failure to this point. This suggests that we may benefit from looking for an underlying cause and means of prevention.
Most people attribute depression and anxiety disorders primarily to life-events or genetics. Dementia is generally assumed to have genetic origins and something we just have to accept as a fact of life.
However, there is epidemiological evidence that some factors in our environment have a massive influence on the development of mental illness and dementia.
According to hospital discharge data, from 1990 to 2010 the incidence of autism, Alzheimer’s disease, celiac disease, sleep disorders, inflammatory bowel disease, and depression all roughly doubled or tripled.
We are a product of our genes and our environment as are all diseases. The human genome didn’t start dramatically changing in 1990, so the increase must be attributed to one or multiple changes in our diets or environment.
The strongest correlation with the rise in diseases is the increase in the use of glycophosphate weed killer on the majority of soy, wheat, and corn we consume. The above charts illustrate a near-identical mirror in the increase in usage of this chemical and incidence of these diseases.
Of course, the rise could also be influenced by many other factors as well. Given that nearly a billion people are suffering daily from all of these diseases combined, it’s extremely urgent that we collect and make publicly available data on the incidence of these disease over time as well as data on all factors that could be exacerbating or improving them.
Currently, there is no easily accessible database such as this. Our goal is to – collect data on every factor that could improve or exacerbate disease
- collect data on every factor (food, drug, supplement, etc) that could improve or exacerbate disease
- analyze it to determine most likely effects of each factor
- make it easy for any layman to see exactly what the most likely positive and negative effects of each product are
Currently, all foods carry nutrition labels such as this one:
But how useful is it to the average person to know the amount of Riboflavin in something? The purpose of nutritional labels is to help individuals make choices that will improve their health and prevent disease. Telling the average personthe amount of riboflavin in something isn’t going to achieve this. This is evidenced by the fact that these labels have existed for decades and during this time, we’ve only seen increases most diseases they were intended to reduce.
We have created a new and improved Outcomes Label that instead lists the degree to which the product is likely to improve or worsen specific health outcomes or symptom. We currently have generated Outcome Labels for thousands of foods, drugs, and nutritional supplements that can be found at studies.crowdsourcingcures.org. These labels are derived from the analysis of 10 million data points anonymously donated by over 10,000 study participants via the app at app.crowdsourcingcures.org.
Although 10 million data points sound like a lot, currently, the usefulness and accuracy of these Outcome Labels are currently limited. This is due to the fact there are only a few study participants that have donated data for a particular food paired with a particular symptom. In observational research such as this, a very large number of participants are required to cancel out all the errors and coincidences that can influence the data for a single individual.
For instance, someone with depression may have started taking an antidepressant at the same time they started seeing a therapist. Then, if their depression improves, it’s impossible to know if the improvement was a result of the antidepressant, the therapist, both, or something else. These random factors are known as confounding variables. However, random confounding factors can cancel each other out when looking at large data sets. This is why it’s important to collect as much data as possible.
These types of data sources can be used to derive the Outcome Labels:
- Individual Micro-Level Data – This could include data manually entered or imported from other devices or apps at app.crowdsourcingcures.org, This could also include shopping receipts for foods, drugs, or nutritional supplements purchased and insurance claim data.
- Macro-Level Epidemiological Data – This includes the incidence of various diseases over time combined with data on the amounts of different drugs or food additives. This is how it was initially discovered that smoking caused lung cancer. With macro-level data, it’s even harder to distinguish correlation from causation. However, different countries often enact different policies that can serve as very useful natural experiments. For instance, 30 countries have banned the use of glyphosate. If the rates of Alzheimer’s, autism, and depression declined in these countries and did not decline in the countries still using glyphosate, this would provide very powerful evidence regarding its effects. Unfortunately, there is no global database that currently provides easy access to the incidence of these conditions in various countries over time and the levels of exposure to various chemicals.
- Clinical Trial Data – This is the gold standard with regard to the level of confidence that a factor is truly the cause of an outcome. However, it’s also the most expensive to collect. As a result, clinical trials are often very small (less than 50 people). Exclusion criteria in trials often prevent study participants from being representative of real patients. There are ethical considerations that prevent us from running trials that have any risk of harm to participants. Due to the expense involved we have very few trials run on anything other than a molecule that can be patented and sold as a drug.