In the first part of this blog series, we reviewed an article that showed us how insomnia is one major risk factor for type 2 diabetes; this week, we explore how other hidden risk factors can influence the development of diabetes and its complications.

Let’s review some of the other risk factors that the authors found. Many of these risk factors are related. The first is depression – is there anything more related to depression and anxiety than insomnia? The second is high systolic blood pressure, associated with age-advanced glycation end products (AGE).  



For many of us, myself included, high blood pressure was the symptom; that’s when you realize that high blood pressure, diabetes, cardiovascular disease, and plaque are all related. Once you begin to look at it, diabetes appears to be related to and probably a cause of high blood pressure associated with age-advanced glycation end products.

How do we use this information? Well, there’s an advanced glycation end product we’ve all heard of called hemoglobin A1c. Most of us know what hemoglobin is. Any protein like hemoglobin will start binding if your blood sugar’s too high. It’s a permanent bond to the protein.

The plasticized proteins can get into the filtering and control mechanisms of the kidneys, decreasing their flow causing high blood pressure.



The authors looked at smoking and caffeine consumption as separate risk factors. I’ve talked about it being healthy, but sometimes it’s a problem. I think caffeine may be associated with insomnia.

Other risk factors are short sleep, daytime napping, and caffeine consumption. They are associated with loss of sleep.

Let me just go through a couple of other points. They are other discussions about different risk factors. If you’re looking for risk factors, you can also look for protective factors.



When you see this kind of image – a tree plot- in a scientific study, you can usually assume that line is the point of differentiation. This line is called a whisker plot – the dot in the middle is where they found the results. Either end is where they found the potential statistical boundaries – two standard deviations. Everything to the left is a risk factor. 

Let’s talk about the protective factors: Birth weight, a fat baby was considered a happy, healthy baby. Then we found out is that’s not the case. For any baby over 8.5lbs, with every ounce over that, you have an increased risk of having prediabetes and diabetes when you get older. It’s called epigenetics; now, the authors found that lower childhood BMI was protective. Other protective factors are testosterone, HDL, years of education, and older age at menarche (first menstruation). 



Let’s summarize the conclusions of this study: They comprehensively assessed causal associations in considerable numbers in type 2 diabetes. Remember that most diabetes is undiagnosed. You get a lot of clouding on the statistics because of that fact. You miss scientific associations if you aren’t aware of most cases. The statistical strength was low – again, these are more epidemiology and statistical issues.

The findings should support the public health policies for primary prevention. That means we need to start thinking about sleep and improving our breathing habits.

A good prevention strategy has its focus on lowering obesity. Don’t think you’re not obese if your BMI is between 25 and 30. Look at your levels of weight. Tim Russert wasn’t – but wasn’t it 30 or more? The rate of smoking is a big deal. If you stop smoking, you are heading in the right direction. Improving mental health and sleep quality. Sleep has been seen before as a risk factor for diabetes. It’s so important. We mentioned education level and birth weight. They are a couple of interesting perspectives on the protection or risk factors for diabetes.



Remember that undiagnosed prediabetes and diabetes are the primary drivers for cardiovascular inflammation, heart attack, and stroke. When it comes to prevention, lifestyle is king



  2. An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study | SpringerLink


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