Disclosures: The NIH supported this study. Dunaif reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.
June 24, 2020
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Novel genetic data reveal two distinct PCOS subtypes

Disclosures: The NIH supported this study. Dunaif reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.
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Researchers have identified two distinct subtypes of polycystic ovary syndrome associated with novel gene regions, a discovery that could improve disease diagnosis and potentially provide new pathways for drug targets.

The findings, published in PLOS Medicine, are based on a mathematical analysis that used clustering of clinical, metabolic and hormonal data from more than 800 women with PCOS, according to Andrea Dunaif, MD, professor and chief of the Hilda and J. Lester Gabrilove division of endocrinology, diabetes and bone disease at the Mount Sinai Health System.

Andrea Dunaif quote

“Everybody talks about PCOS being a heterogeneous disorder, so we decided to formally test this hypothesis,” Dunaif told Healio. “We performed a cluster analysis, which is a mathematical approach that has no predetermined assumptions about the data and aggregates groups of data that appear to be similar. We are just starting to see this type of analysis in genetics, because we are learning that diseases like PCOS, diabetes and obesity are heterogeneous, and there probably are subtypes. The big strength of our data set, which we have been assembling since 1996, is we very carefully phenotyped the patients. Our data was highly suitable for this analysis.”

Genetic clues

Using clinical, biochemical and genotype data from their previously published PCOS genome-wide association study (GWAS), Dunaif and colleagues conducted cluster analysis in the GWAS cohort of 893 PCOS cases (median age, 28 years; median BMI, 35.4 kg/m²). The clusters were then replicated in an independent, ungenotyped cohort of 263 PCOS cases (median age, 28 years; median BMI, 35.7 kg/m²).

The clustering revealed two distinct PCOS subtypes: a “reproductive” group (21% to 23%) characterized by higher luteinizing hormone (LH) and sex hormone-binding globulin with relatively low BMI and insulin levels; and a “metabolic” group (37% to 39%) characterized by higher BMI and glucose and insulin levels with lower SHBG and LH levels, Dunaif said.

“This is something that investigators in the field have suspected, but none of us had previously been able to robustly characterize it — that there are women who seem to have higher LH levels, lower body weight, higher testosterone levels consistent with the reproductive subtype,” Dunaif, who is an Endocrine Today Editorial Board Member said. “Then there are individuals who are more obese, with higher insulin and higher glucose levels, and that is the metabolic subtype. One could think that, perhaps, these subtypes represent extremes of a normal distribution, but what the cluster analysis allows you to test using an unbiased approach is whether these traits aggregate with each other.”

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The researchers performed a GWAS on the genotyped cohort, limiting cases to either reproductive or metabolic phenotypes. Researchers identified alleles in four loci associated with the reproductive subtype at genome-wide significance, as well as one locus associated with the metabolic subtype. Researchers then developed a predictive model to classify a separate, family-based cohort of 73 women with PCOS and found that the subtypes tended to cluster in families.

Dunaif said that the effect sizes for the loci associated with the subtypes were surprisingly large, particularly for those associated with the reproductive phenotype.

“Usually, in GWAS, we see that the associated gene loci regions may increase risk for disease by 10% to 15%,” Dunaif said. “In fact, what’s been a big disappointment in the field, is that each gene seems to contribute so little to the final phenotype. Here, the increased risk was threefold to fivefold. Now we have to replicate that finding, and we are funded to start doing that, but if the effect sizes are anywhere in this range, the findings are extremely exciting.”

Screening, treatment possibilities

Dunaif said the findings could lead to better screening and treatment options for women with PCOS.

“No. 1, when a gene has such a strong signal, it should be easier to identify it when conducting molecular studies,” Dunaif said. “No. 2, if it plays such a big role, that suggests that whatever pathway it disrupts is an important pathway for drug targeting. No. 3, if it is also a very strong signal and predictive, then it is a good gene for using as a disease marker.”

This current paper builds on a study that Dunaif and colleagues published in The Journal of Endocrinology and Metabolism in April 2019 in which they found, using family-based genetic analysis, that rare genetic variants in a gene involved in male hormone production, DENND1A, play major roles in the development of PCOS. This genetic variation could serve as a marker for early PCOS detection, she said.

“In 2018, we were part of the largest GWAS meta-analysis to date in PCOS with over 10,000 cases. This study was a large enough study to compare subsets of PCOS defined by the current diagnostic criteria,” Dunaif said. “That study found there were no significant genetic differences among the PCOS phenotypes defined by the diagnostic criteria, which is the opposite of what we found here. It shows that the PCOS phenotypes defined by the diagnostic criteria, which are based on expert opinion, do not identify genetically distinct subgroups of PCOS cases. With genetics, we are starting to understand the genes and pathways that cause PCOS, so science, rather than opinion, can inform our disease classification. That will be the future.”