AsianScientist (Jul. 19, 2016) – The focus of World Health Day 2016 is the “rising tide of diabetes,” and with good reason. An estimated 350 million people worldwide have diabetes, a number that is likely to more than double in the next 20 years, according to the World Health Organization (WHO).
Singapore in particular, while boasting a life expectancy in the 80s, has the second-highest proportion of diabetics among developed nations, according to a report by the International Diabetes Federation (IDF). This problem needs to be addressed, fast. Singaporeans are getting sicker, starting from a younger age. According to the Diabetic Society of Singapore, one out of nine people aged 18 to 69 has diabetes, comprising about 11.3 percent of the population or more than 400,000 people.
Associate Professor Tai E Shyong, head and senior consultant of the division of endocrinology at the National University Hospital in Singapore, believes that there is a smarter way to design treatments for diabetes and other chronic diseases. Tai, who has been studying the genetics of complex diseases for most of his career, believes that personalized medicine opens up opportunities for researchers to create targeted treatments for specific groups of people, for greater effectiveness and improved patient outcomes.
- Why are non-communicable diseases (NCDs) like diabetes becoming more prevalent in Asia?
I think it’s primarily because we just live longer, particularly here in Singapore. But across the region, life expectancies have increased rapidly.
If you look at the burden of disease, you’ll find that in populations where the life expectancy is [around] 20-30 years, the major causes of death are infectious diseases and nutritional disorders. The moment we start to get into populations that live into their 80’s, you find that they die of chronic disease, NCDs, cancer, diabetes and cardiovascular disease.
That’s really important because we have to remember that the healthcare systems in most countries in the world were set up at a time when nutritional deficiencies, infectious diseases and trauma were the major causes of disease burden, and those are fundamentally different from NCDs. [Previously] you would treat patients who would get better and then, they’re okay. Whereas when you get an NCD, you’re talking about something that is life-long.
- How can healthcare systems brace for this shift towards life-long diseases?
What we have here is a healthcare system that was engineered—and this is not unique to Singapore—to provide episodic care. Continuity was not important, and so the healthcare system wasn’t designed for that. [We] need to shift the model of care, because the current one is designed for episodic care, giving rise to a lot of fragmentation.
People receive care from multiple institutions, sometimes at the same time, but very often, one after the other. It’s during that transition that information gets lost and inefficiencies start to creep into the system.
In countries that are very poorly developed in terms of healthcare, you very rapidly see a corresponding increase of improvement in outcomes as they start to increase their healthcare budget. However, you will reach a point where it plateaus off, and as you continue to increase healthcare expenditure, you are either getting no increments in outcomes or very small increments.
Once you reach that point, you will need to fundamentally redesign the system. What we’re trying to do [with personalized medicine] is to shift the trajectory, so that the more we put in, the more we get out.
- In the case of diabetes, how can personalized medicine improve patient outcomes?
Imagine that there are six different types of diabetes medications, and usually if you don’t respond to one, you’re not going to respond to any of the others. If that’s the case there’s nothing to do; there’s no personalization.
As it turns out, that’s not the case—some people actually respond to one drug, and not to the other. We need to figure out who these people are and give them the drugs that work for them. For the drugs that are not likely to work, it helps to know that early so you can take the patients off them, rather than just keep adding on more drugs.
Here is another example: One of the biggest problems of diabetes today is chronic kidney disease (CKD). About a third of the patients with diabetes eventually develop CKD. For every patient with some evidence of diabetic kidney disease, about 20 to 30 percent progress to CKD, another 20 to 30 percent will get better, and then 20 percent stay the same. But we treat them all the same.
Is that really the most effective way? We don’t really know. But it’s attractive to think that if we are able to identify the patients who are going to get better, we should just wait rather than add any more therapy.
- How can personalized medicine help to alleviate disease burden at the scale of the national healthcare system?
First of all, if you think about personalized medicine as something different for every individual, that’s a tough concept; science is not going to deliver that. We can never imagine ever having a situation where everyone needs a different drug and we’re going to develop a drug just for this person.
In terms of being able to be a little bit smarter about what we do, it’s always going to be groups of people rather than individuals. First, we will need to segment the population and identify groups of individuals who don’t respond to any existing therapies. Then we can study them and figure out what’s happening with these people, so we can design a drug that will work for them.
- Could you give us an example of how a population can be segmented?
If you have high low-density lipoprotein (LDL) cholesterol, for example, you have an increased risk of heart disease. If you take a cholesterol-lowering drug such as a statin, it will reduce your risk by 25-30 percent. But we don’t give statins to everybody—we give statins to people who are at higher risk of heart disease.
The reason is that we have to balance the reduction of risk of heart disease with the risk arising from side effects of the drug. If you had a previous heart attack, we know that you are very likely to have another one and will give you a high-dose statin, which comes at a cost—increased risk of side effects. But because you’re using it in a person with a very high risk of heart disease, let’s say 20 percent over the next 10 years, if I reduce the risk of a heart attack by 30 percent, that’s a six percent reduction.
In contrast, the risk of side effects is maybe two percent. So overall, there’s a positive cost-benefit relationship.
As opposed to [a young woman], whose risk of heart disease is maybe 0.2 percent over the next ten years; if I give her a statin, I will get a 30 percent reduction but she only had a 0.2 percent risk of a heart attack in the first place, so now she’s at 0.14 percent. But there’s still a two percent risk of side effects; so I don’t treat her.
That’s how statins are prescribed. We basically say that intensive therapies are given to the people with the highest risk, and therefore this is the most cost-effective way to deploy these therapies.
- When it comes to the delivery of personalized medicine, what are the main barriers and challenges that need to be addressed?
There are three aspects. The first is creating the infrastructure required to do data analytics, overcoming all sorts of issues around data privacy, protection, security, and how we share data. A lot of this is becoming increasingly complex [with] the 2012 Personal Data Protection Act.
Secondly, nowadays, we’re looking very closely at ethics and regulation. What do we do with the data? How do we share it? Who do patients want it to be shared with? How do we physically do that?
We would need a protective data cloud for housing data that would allow people with the right access to come in and run analyses, but at the same time, stop them from downloading data sets so they can only take out aggregate data. Obviously the system needs to first be built; I think that’s a barrier, something we need to work on.
Thirdly, there’s cost. You need to think very early on: is what I do likely to be cost-effective and who’s going to pay for it? I think that’s a very real challenge. Providing care that will keep people out of hospital—who pays for that? How does it get to the person who’s delivering the care?
I think that’s a big struggle because a lot of prevention is hoping to reduce future costs; that’s not a tangible sort of thing. It’s like, if I didn’t do this, it might cost you $100,000 more in the future; now give me $50,000. That’s a tough argument.
- What can patients expect from personalized medicine in the years to come?
What I am most looking forward to is better stratification based on the use of data. I think with more data, whether it be traditional or biological—I’d like to think it’s a combination of both—patient subgroups will become smaller and smaller and have greater resolution.
Having said that, if we think about chronic disease as being a condition where continuity of care is important, then it’s also important that we deliver healthcare that is based on a shared decision with the patient—where as a doctor, you get to know your patients. This is so that I know that the reason your blood sugar went up last week was because your dog died. Then, there is a very good opportunity to individualize care.
Individualized care occurs at the level of a patient-physician relationship, and it comes from years and decades of getting to know a patient’s likes and dislikes, what their aspirations are. This year, we will be trying out a program of individualized care. It’s based on something called motivational interviewing—basically getting your healthcare professional to work with the patient and participate in decision-making processes, setting joint goals. The idea is that when you get better engagement, you see better outcomes.
I think this can work, but the key is trying to figure out how to build that one-on-one relationship in a health system where we never had personal physicians.
This article was first published in the print version of Asian Scientist Magazine, July 2016.
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Copyright: Asian Scientist Magazine. Photo: National University of Singapore Yong Loo Lin School of Medicine.
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