Paternal history of diabetes mellitus and hypertension affects the prevalence and phenotype of PCOS

Risk Factors for Incident Polycystic Ovary Syndrome Diagnosis

Objective: While highly prevalent, risk factors for incident polycystic ovary syndrome (PCOS) are poorly delineated. Using a population-based cohort, we sought to identify predictors of incident PCOS diagnosis. Materials and Methods: A matched case-control analysis was completed utilizing patients enrolled in Kaiser Permanente Washington from 2006 to 2019. Inclusion criteria included female sex, age 16-40 years, and ≥3 years of prior enrollment with ≥1 health care encounter. PCOS cases were identified using International Classification of Diseases codes. For each incident case (n = 2,491), 5 patients without PCOS (n = 12,455) were matched based on birth year and enrollment status. Potential risk factors preceding diagnosis included family history of PCOS, premature menarche, parity, race, weight gain, obesity, valproate use, metabolic syndrome, epilepsy, prediabetes, and types 1 and 2 diabetes. Potential risk factors for incident PCOS diagnosis were assessed with univariate and multivariable conditional logistic regressions. Results: Mean age of PCOS cases was 26.9 years (SD 6.8). PCOS cases, compared with non-PCOS, were more frequently nulliparous (70.9% versus 62.4%) and in the 3 years prior to index date were more likely to have obesity (53.8% versus 20.7%), metabolic syndrome (14.5% versus 4.3%), prediabetes (7.4% versus 1.6%), and type 2 diabetes (4.1% versus 1.7%) (p < 0.001 for all comparisons). In multivariable models, factors associated with higher risk for incident PCOS included the following: obesity (compared with nonobese) Class I-II (body-mass index [BMI], 30-40 kg/m2; odds ratio [OR], 3.8; 95% confidence interval [CI], 3.4-4.2), Class III (BMI > 40 kg/m2; OR, 7.5, 95% CI, 6.5-8.7), weight gain (compared with weight loss or maintenance) of 1-10% (OR, 1.7, 95% CI, 1.3-2.1), 10-20% (OR, 1.9; 95% CI, 1.5-2.4), and >20% (OR, 2.6; 95% CI, 1.9-3.6), prediabetes (OR, 2.7; 95% CI, 2.1-3.4), and metabolic syndrome (OR, 1.8: 95% CI, 1.5-2.1). Conclusion: Excess weight gain, obesity, and metabolic dysfunction may play a key role in the ensuing phenotypic expression of PCOS. Treatment and prevention strategies targeted at preventing weight gain in early reproductive years may help reduce the risk of this syndrome.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Family history of menstrual irregularity or diabetes mellitus enhances the susceptibility to polycystic ovary syndrome among subjects harboring rs7903146 genetic variant of TCF7L2

Purpose

TCF7L2 mediated Wnt signaling cascade regulates glucose homeostasis by orchestrating expression, processing, and hepatic clearance of insulin. Type 2 diabetes mellitus (T2DM) and polycystic ovary syndrome (PCOS) significantly overlap in pathophysiological features with insulin resistance as a central driver. While TCF7L2 is the most potent T2DM locus, studies on the association of TCF7L2 with PCOS are limited and inconclusive. Therefore, in addition to expression profiling, the association of TCF7L2 polymorphic variant rs7903146 with PCOS was evaluated.

Methods

Using Rotterdam-2003 criteria for the diagnosis, 120 PCOS cases, and 120 age-matched controls were recruited. Subjects underwent clinical, biochemical, and hormonal assessment, followed by genotyping for rs7903146, carried out by PCR-RFLP and TCFL2 expression profiling by qRT-PCR. Genotype-phenotype correlation analysis was performed to evaluate any such associations. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were computed by conditional logistic regression.

Results

Higher odds of developing PCOS were observed in the women having a family history (FH) of either T2DM (OR = 3.86, 95% CI:1.90 - 7.83), hirsutism (OR = 4.74. 95%CI: 1.91 - 17.21) or menstrual irregularities (MI) (OR = 3.07, 95%CI: 1.61 - 8.54). The genotypes of rs7903146 did not confer any risk for developing PCOS (OR = 0.46;95%CI: 0.15 - 2.03). However, the elevated risk was seen in the subjects who harbored the variant allele and had FH of either T2DM (OR = 6.71; 95%CI: 1.89 - 23.78) or MI (OR = 9.71; 95% CI:1.89 - 23.78).

Conclusion

TCF7L2 polymorphic variant rs7903146 is not independently linked to PCOS risk, but modulates the risk in the subjects having a family history of either T2DM or MI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-022-01050-y.

Morning blood pressure surge and its relation to insulin resistance in patients of reproductive age with polycystic ovary syndrome

BACKGROUND

Our aim in this study was to investigate morning blood pressure surge (MBPS) in patients of reproductive age with polycystic ovary syndrome (PCOS) and its relation to insulin resistance (IR).

METHODS

Fifty-three patients with PCOS without additional illness were included in the study. Forty-two age-matched subjects without PCOS were selected as the control group. All study subjects underwent 24-h blood pressure monitoring. Patients with additional illnesses, drug users, smokers, and alcohol and drug abusers were excluded. Blood insulin, fasting glucose, lipid profile, and hormone profile were measured. Insulin resistance was calculated using the HOMA-IR formula.

RESULTS

Median age (years) was 27 (20-33) in the PCOS group and 27 (22-33) in the control group. Body mass index was higher in the PCOS group. Office systolic and diastolic blood pressure was higher in the PCOS group. Mean awakening 2-h BPs (mmHg) was 110 ± 7 in the control group and 118 ± 5 in the PCOS group (p < 0.001). Mean MBPS (mmHg) was 21 ± 6 in the control group and 29 ± 8 in the PCOS group. Mean MBPS was higher in the PCOS group (p < 0.001). IR was more frequent in the PCOS group. Based on logistic regression analysis, the presence of PCOS and IR were independent predictors for MBPS.

CONCLUSIONS

The results of our study showed that MBPS increased excessively when compared to non-PCOS controls in young women with PCOS during reproductive age. In addition, PCOS and insulin resistance were independent risk factors for exaggerated MBPS.

Transcription factor‑microRNA synergistic regulatory network revealing the mechanism of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is the most common type of endocrine disorder, affecting 5–11% of women of reproductive age worldwide. Transcription factors (TFs) and microRNAs are considered to have crucial roles in the developmental process of several diseases and have synergistic regulatory actions. However, the effects of TFs and microRNAs, and the patterns of their cooperation in the synergistic regulatory network of PCOS, remain to be elucidated. The present study aimed to determine the possible mechanism of PCOS, based on a TF-microRNA synergistic regulatory network. Initially, the differentially expressed genes (DEGs) in PCOS were identified using microarray data of the GSE34526 dataset. Subsequently, the TFs and microRNAs which regulated the DEGs of PCOS were identified, and a PCOS-associated TF-microRNA synergistic regulatory network was constructed. This network included 195 DEGs, 136 TFs and 283 microRNAs, and the DEGs were regulated by TFs and microRNAs. Based on topological and functional enrichment analyses, SP1, mir-355-5p and JUN were identified as potentially crucial regulators in the development of PCOS and in characterizing the regulatory mechanism. In conclusion, the TF-microRNA synergistic regulatory network constructed in the present study provides novel insight on the molecular mechanism of PCOS in the form of synergistic regulated model.