| Literature DB >> 35865312 |
Yue Xu1, Zhi-Yang Zhou1, Jie-Xue Pan1, He-Feng Huang1,2,3.
Abstract
A potential correlation between polycystic ovary syndrome (PCOS) and asthma, used to be identified as diseases originating from two independent systems, has been supported by increasing evidence. From an epidemiological perspective, mounting studies have confirmed that women suffering from PCOS exhibit increased susceptibility to asthma. Meanwhile, PCOS and asthma seem to share several mutual pathological conditions, such as metabolic disorders, hormonal fluctuation, proinflammatory state, etc. Here, we further elucidate the correlation between asthma and PCOS by focusing on the internal common pathophysiology and adverse influences on women's health. Understanding the internal connection between PCOS and asthma may shed light on developing new prevention and control strategies to fight against these conditions.Entities:
Keywords: asthma; chronic inflammation; metabolic syndrome; polycystic ovary syndrome; reproductive health
Mesh:
Year: 2022 PMID: 35865312 PMCID: PMC9294161 DOI: 10.3389/fendo.2022.936948
Source DB: PubMed Journal: Front Endocrinol (Lausanne) ISSN: 1664-2392 Impact factor: 6.055
Figure 1Potential associations between PCOS and asthma. Women with PCOS or asthma are characterized by several common disease phenotypes, including metabolic syndrome, irregular menstruation, impaired fertility and mood disturbances. And chronic local and systemic inflammation may act as one of the common molecular mechanisms behind these phenomena. Image was created with Biorender®.
General characteristics of key studies.
| Author | Year | Country | Study design | PCOS criteria | Asthma criteria | Characteristics of the subjects | Asthma incidence % (n) | P value | ||
|---|---|---|---|---|---|---|---|---|---|---|
| PCOS | Control | PCOS | Control | |||||||
| Hart et al. | 2015 | Australia | Retrospective cohort study | ICD-10 | Medical | Median age: 27.9 | Not available | 10.56% | 4,52% | <0.01 |
| Underdal et al. ( | 2020 | Norway | Prospective cohort study | Rotterdam | Medical | Mean age: 38 | Mean age: 38 | 19.31% | 8.74% | <0.01 |
| Doherty et al. | 2015 | Australia | Retrospective cohort study | ICD-10 | Medical | Not available | Not available | 13.64% | 9.90% | <0.01 |
| Zierau et al. ( | 2018 | Denmark | Prospective cohort study | Rotterdam | Medical | Mean age: 34 | Mean age: 34 | 19.59% | 14.48% | <0.001 |
| Glintborg et al. | 2015 | Denmark | Prospective cohort study | ICD-10 | Medical | Mean age: 30.6 | Mean Age: 30.6 | 3,05% | 2.21% | <0.001 |
| Htet et al. | 2017 | Australia | Prospective cohort study | Questionnaire | Questionnaire | Age: 30.5 ± 0.1 | Age: 30.6 ± 0.02 | 16.11% | 10.52% | 0.004 |
| Grieger et al. ( | 2020 | Australia | Prospective cohort study | Questionnaire | Questionnaire | Age: 33.5 ± 1.4 | Age: 33.7 ± 1.5 | 13.64% | 9.90% | 0.004 |