Analyzing Alzheimer’s

by | Oct 23, 2020 | Analytics, Industry Trends

Analyzing Alzheimer’s Disease

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction 

Alzheimer’s disease is a progressive disease that sees cells and cell connections die resulting in the loss of memories and mental function. It is a condition that affects 5 million Americans, with projections to affect 14 million people by 2050 and while there are medications to help treat symptoms, there is no cure. When trying to battle diseases without a cure, the best thing to do is to look at prevention and applying data science to that process can improve our understanding of how mitigating factors can benefit us individually. It can also help scientists create targeted gene therapies to help those affected by Alzheimer’s.   

Discussion 

Currently, a lot of the research being done in regards to Alzheimer’s disease revolves around genetics because of the genetic expressions and patterns that seem to be consistent in many of those that have the disease. To apply analytics to this knowledge, researchers at Icahn School of Medicine at Mount Sinai and Emory University formed a joint ventures with other research institutions to perform deep data analysis on mined DNA, RNA, protein, and clinical data. With this data they hoped to identify regulators and predictors of the disease in order to create targeted gene therapy treatments. What they found was that, though there are correlations between genetic expressions and Alzheimer’s, they were not strong enough to identify a singular cause. However, they did find that a protein called VGF plays an “plays an important role in protecting the brain against the onset and progression of Alzheimer’s disease.” Once they identified this connection, scientists could create a testing environment in which they “ramp[ed] up levels of the gene or protein in mice” and saw that those mice had a significantly lower risk of having Alzheimer’s or saw the progression of their disease slow down. Another gene therapy study out of Stanford University made a similar connection with the ApoE4 gene variant which is present in more than half of Alzheimer’s patients. Again, they did not find this to be a direct cause of the disease but prevalent enough that increased expressions of that gene increases the risk of Alzheimer’s. Studies like these are extremely important because understanding our genetic risk factors can help us understand how dedicated we should be to focusing on mitigating factors.

Conclusion 

Alzheimer’s is a difficult disease to live with and, though genetic risk factors are unavoidable, living a healthy lifestyle can help prevent Alzheimer’s by lowering blood pressure and cholesterol, as well as lowering the risk of contracting diabetes, all of which have been connected to Alzheimer’s. Incorporating healthy foods and exercise into your lifestyle can help support these goals. Additionally, staying mentally active by continuing to learn and keeping up social connections has been shown to decrease the risk of Alzheimer’s as well. Finally, avoiding head trauma by taking precautions such as wearing a seatbelt, wearing a helmet and avoiding falls are all important steps you should take. Though we are discussing these prevention steps in terms of Alzheimer’s, they are all important steps to mitigating many other conditions. 

Studying Our Genes to Understand the Impacts of Covid-19

Studying Our Genes to Understand the Impacts of Covid-19

by | Oct 14, 2020 | Covid, Industry Trends, Technology

Studying Our Genes to Understand the Impacts of Covid-19
Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Here at Altheia Predictive Health, we analyze several factors when making disease predictions; one of these factors is family history and DNA markers which are parts of looking at genetic predispositions. Some companies dive deeper into DNA markers as a primary indicator for other conditions, such as how the BRCA1 gene relates to breast cancer in women. Similarly, genetics is now being used to understand if certain people may be at a higher risk of suffering serious complications from Covid-19 due to certain genetic expressions. Studies are also being conducted to understand if and how Covid-19 may change or mutate genetic expressions. In this article, we will take a look at some of the research being performed in this area. 

Discussion

When it comes to looking at gene expression as it relates to Covid-19, researchers are not simply looking at who is most likely to test positive for the disease because many people are simply asymptomatic carriers. Rather, they are looking at gene expressions related to patients who have dealt with severe symptoms, or even passed away, as a result of Covid-19. A recent study published in the scientific journal, Nature Research, discussed a “genetic association study [that] identified a gene cluster on chromosome 3 as a risk locus for respiratory failure after infection with severe acute respiratory syndrome coronavirus 2.” The study went one step further and mapped this genetic expression to find that it was actually inherited from Neanderthals and “is carried by around 50% of people in south Asia and around 16% of people in Europe.” 

Another study, out of the University of Edinburgh, went a step further to better understand the strength of impact this marker and found that “because 74% of patients [with the marker] were so sick that they needed invasive ventilation, it had the statistical strength to reveal other markers, elsewhere in the genome, linked to severe COVID-19; and that a single copy of the associated variant more than doubles an infected person’s odds of developing severe COVID-19.” 

For those who survive a tough battle against Covid-19, as well as for those who test positive but are asymptomatic, the question remains of whether or not the disease can cause long term damage to our genes. Google recently granted researchers at the University of North Carolina – Chapel Hill $500,000 to study if and how Covid-19 alters gene expression. To conduct this research, researchers will “ compare RNA, a marker of gene expression, from the blood collected over years from the same individuals before and after COVID-19 infection [and]… use artificial intelligence tools to scan the genome for changes in gene expression that may be due to COVID-19 infection.”  

Conclusion 

Similar to many other health issues, our genetics can clearly play a huge role in how successfully we battle a disease such as Covid-19. What is promising about the research being conducted in this area is that once we better understand who is at the highest risk, we can better protect them and even create gene therapies to prevent severe symptoms or death due to Covid-19 for these people. However, just because you are not at risk of suffering from severe symptoms is no sign to put away your mask – Covid-19 has already been shown to have long term effects on those who test positive while asymptomatic and further studies will help us understand the severity of how the disease alters our genetic expression.

 

 

Using Big Data to Improve the Lives of Liver Disease Patients

by | Sep 23, 2020 | Analytics, Industry Trends, Technology

Using Big Data to Improve the Lives of Liver Disease Patients

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction 

The liver is an extremely important organ in our bodies and is responsible for several tasks; of its many functions the most important tasks is its filtration and detoxification functions. The liver also helps regulate blood sugar, breakdown fat, produces and recycles blood at certain points in our lives, and many, many other supporting functions. Clearly, the liver performs key actions to keep our bodies healthy and functioning so anything that decreases its capabilities, such as liver disease, is important to look at. Liver disease is a very broad term and refers to any issue that affects the liver’s ability to function. Liver issues are classified as a disease when 75% or more of the liver tissue is affected which is when decrease in function begins. It can be caused by one or many factors – infections such as hepatitis, autoimmune diseases, certain cancers, genetics, alcohol abuse and increased fat accumulation can all play a role in the onset of liver disease. The use of data to analyze these factors, as well as metabolic factors, is very key to improving the diagnosis and management of liver disease. 

Discussion 

There are a few quantifiable factors that are related to liver disease; those factors include: Age,Gender, Total Bilirubin, Direct Bilirubin, Alkaline Phosphatase, Alanine Aminotransferase, Aspartate Aminotransferase, Total Proteins, Albumin and Albumin and Globulin Ratio. All of these variables have been positively correlated to the presence of liver disease and would be important factors for any algorithm that looks at liver disease or other diseases that compromise liver function. The Global Journal of Computer Science and Technology published a report in 2010 that looked at how to apply statistical modeling and machine learning to the study of liver disease. They used three different supervised algorithms – Naive Bayes, KStar and FT Trees – to predict the accuracy of liver disease diagnoses and found that FT Trees provided the highest level of accuracy at 97.10%. Such a high accuracy rate provides a solid foundation for other researchers to add in new variables and factors to further improve that rate. Also in this area of research is a project led by Harvard Medical School, Massachusetts General Hospital and Georgia Institute of Technology that has set out to further understand the effects of alcohol in relation to liver issue related deaths. The project begins by modeling drinking patterns against alcohol based liver issues in patients born from 1900-2012 and then utilized different intervention scenarios to see if reducing alcohol consumption also lowered chances of liver issue related deaths. The findings, in line with most practical medical advice, found that liver function decreased with alcohol consumption and its functionality improved more with each intervention that happened sooner than later.  

Prevention

The most important thing you can do to keep your liver healthy is to eat healthy foods and live a healthy lifestyle, including reduction of alcohol consumption. There are several genetic factors that, unfortunately, cannot be mitigated, however, having genetic tests done so that you are aware of any increased risk you may have is critical and can help you determine how much you may need to adjust your lifestyle to accommodate those factors. 

Conclusion 

Similar to many other diseases, liver disease is quantifiable in many ways. There are already several promising studies that have created a solid foundation for further research in the area of machine learning applications to this field of study; additionally, there are also promising studies that show the importance and influence of lifestyle interventions in changing the success rate of patients suffering from liver disease.

 

 

Prescriptive Analytics

Prescriptive Analytics

by | Aug 5, 2020 | Analytics, Covid, Industry Trends

Prescriptive Analytics

 

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction

Here at Altheia Predictive Health, we are constantly improving techniques for disease prediction and diagnosis to improve the lives of people around the world. Where we leave off, companies like IBM pick up and begin applying analytics to the next step in the disease management process which is a treatment plan. Prescriptive analytics in healthcare can refer to several different things but in this article, we will look at prescriptive analytics as it related to the study of prescribed steps for those with chronic conditions to better understand how to manage disease conditions at various stages of a given condition. This can be an extremely useful tool for doctors who are looking at a borderline case and deciding whether or not to begin medication treatments by giving them the ability to see compiled and analyzed data for similar cases.

Discussion

After being diagnosed with a disease, the next question patients and providers have is what steps can be taken to prevent progression of the disease and manage the negative effects of a disease; for some people this is achieved through diet and exercise, for others it may be a prescribed medication and it is highly dependent on several patient factors. Prescriptive analytics is rooted in how to best solve a problem – if the problem is diabetes, a prescriptive model would look at variables in a patient’s file to determine if the problem can be mitigated through diet and exercise. If not, the model would then provide a game plan for how to begin prescribing medicines in the most effective way for any given patient. In diabetes patients, insulin therapy is a common form of treatment where adjustments to a specific individual can be tricky but prescriptive analytics can use the data of similar patients to provide a tried and true treatment plan.  Prescriptive analytics can also be used in radiation therapy by weighing various favors, such as treatment location and surrounding organs, to provide a highly targeted treatment plan that treats an area with a calibrated dosage while leaving the rest of the body as unharmed as possible. This is something that we as humans can make educated guesses on but the use of prescriptive analytics is likely much more accurate which means better care for patients. 

This field of study also has an incredibly relevant application amidst the Covid-19 Pandemic. Throughout the pandemic data has been collected on patients such as their age, BMI, gender, ethnicity, presence of chronic conditions and more. All of this information combined with their response to treatment plans provides a unique opportunity to apply analytics – by looking at these sets of data together, we can use artificial intelligence to understand which patients respond better to certain treatment plans. This provides a great opportunity to improve Covid-19 survival rates and lower the number of patients suffering from long-term complications as a result of Covid-19. 

Conclusion

Prescriptive analytics in healthcare is essentially a way for analytics to troubleshoot disease conditions by providing a data-based treatment plan. By providing physicians with analytical tools, such as prescriptive analytics, we can significantly improve their success rate in treatment plans and as a result improve the quality of life for hundreds of thousands of people.