Goodarzi and his team did a causality analysis, examining the relationship between PCOS and diabetes using the Mendelian randomization (MR) technique to adjust data for genetic factors. MR is a technique for using genetic variants to analyze causality and disease. “When I first learned about the MR technique, as a geneticist I found it quite
Goodarzi and his team did a causality analysis, examining the relationship between PCOS and diabetes using the Mendelian randomization (MR) technique to adjust data for genetic factors. MR is a technique for using genetic variants to analyze causality and disease.
“When I first learned about the MR technique, as a geneticist I found it quite exciting,” says Goodarzi. “MR gives the investigator a means to use genetics to learn about biology even if the way the genetic variants work in the cell is unclear. If the researcher knows that the variant is a strong risk factor for the exposure, the technique works.”
In a classic randomized clinical trial, the investigator randomizes half of the subjects to intervention—a drug therapy or otherwise—and half of the subjects to placebo, and then follows those subjects for a couple of years to see whether the intervention effected any change. In MR, the randomization reflects the random assortment of genetic variants an individual receives from their parents. The key benefit is that this exposure, the genetic variants, is lifelong, as opposed to a clinical trial which may last four or five years. In Goodarzi’s study, for example, PCOS is the exposure and diabetes, heart disease and stroke are the outcomes. Using MR, Goodarzi assigned the particular variants for PCOS as the exposure, obtained data from large, previously published studies and then used analysis to obtain a causal estimate for each outcome.
This type of genetic analysis has been done on causal relationships in many different fields. For example, obesity factors into both diabetes and heart disease—and has been proven to have a causal relationship by this type of study with MR. The medical community now takes that causal relationship for granted.
As it turns out, there are key features of diabetes that are also present in patients with PCOS. The first is obesity. Data indicates that between 30% and 80% of women with PCOS are also overweight or obese. Obesity is well established, with epidemiologic and genetic evidence, as a causal factor for diabetes and heart disease.
Prior to this research, Goodarzi and his team had published a paper using MR to study the causal relationship between PCOS and obesity. The research found that obesity—measured by BMI—was a causal factor for PCOS. However, the reverse is not true: PCOS does not cause obesity. This represents an important finding for the field of endocrinology, which often grapples with obesity and its causal relationships with various other conditions.
“If BMI causes heart disease and diabetes, and BMI causes PCOS, that explains the association between PCOS and diabetes observed by physicians,” explains Goodarzi.
Additionally, there is published genetic evidence that indicates that elevated testosterone in women is a causal factor for both PCOS and diabetes. The same is true for low levels of a blood protein called SHBG—low levels of SHBG is a causal factor for both diseases.