Abstract
Research Question:
Data suggest that patients with polycystic ovary syndrome (PCOS) are at increased risk of developing preeclampsia; however, several studies have not found an independent association between the two. This study aimed to quantify the association between PCOS and preeclampsia.
Design:
A retrospective case-control study of singleton deliveries at a tertiary care hospital from 2011–2015 was performed. Patients with preeclampsia (cases) were matched to the next delivery without preeclampsia (controls) on gestational age week. Medical history data, a diagnosis or clinical features of PCOS, and obstetric data, including preeclampsia, were abstracted from the medical record. Groups were compared with the Chi-square test, and conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). Odds ratios were adjusted for maternal age at delivery and race/ethnicity.
Results:
This study included 435 cases and 435 controls. Cases were more likely to be Black compared to controls. Age, comorbidities, features of PCOS, and use of in vitro fertilization were similar between groups. Patients with preeclampsia were not more likely to have PCOS (8.3%) than those without preeclampsia (6.2%, adjusted OR 1.4, 95% CI 0.81 – 2.3). Sensitivity analyses for BMI and parity suggested an increased preeclampsia risk for patients with PCOS and these additional factors, however no group showed a statistically significant association between PCOS and preeclampsia.
Conclusions:
In this study, a history of PCOS was not associated with the risk of preeclampsia. Further investigation is necessary to determine if there are subgroups of PCOS patients who are at increased risk of preeclampsia.
INTRODUCTION
Polycystic ovary syndrome (PCOS) affects 6–18% of reproductive-aged female patients and is the most common endocrinopathy in this demographic, leading to $8 billion in health care spending annually (Balen et al., 2016; Fauser et al., 2012; Riestenberg et al., 2021). PCOS is most commonly diagnosed using the Rotterdam Criteria, which requires two of the following: oligo- or anovulation, clinical and/or biochemical signs of hyperandrogenism, or polycystic ovaries, plus exclusion of other etiologies such as congenital adrenal hyperplasia, androgen-secreting tumors, growth hormone secreting pituitary tumors, and Cushing’s syndrome (Balen et al., 2016). PCOS presents with a wide range of clinical phenotypes, which may differ across ethnic groups, with some groups more likely to exhibit hirsutism or obesity (Balen et al., 2016; Dumesic et al., 2015; Fauser et al., 2012).
The broad spectrum of clinical phenotypes poses a unique challenge to both patient care and medical research alike (Fauser et al., 2012). PCOS is associated with insulin resistance, hypertension, dyslipidemia, infertility, and adverse pregnancy outcomes (Balen et al., 2016; Lo et al., 2006; Palomba et al., 2015). Women with PCOS are at increased risk of miscarriage, hypertensive disorders of pregnancy, gestational diabetes, and preterm delivery, and this risk varies with PCOS phenotype (Palomba et al, 2010; Palomba et al, 2015).
Preeclampsia affects 3–5% of pregnant women and is thought to have two subtypes: early-onset, resulting from the upregulation of placental antiangiogenic factors and maternal, or late-onset, resulting from maternal endothelial dysfunction leading to microvascular damage. Preeclampsia increases the risk of future hypertension, metabolic disease, and cardiovascular disease (Phipps et al., 2016). Risk factors for preeclampsia, in turn, include obesity, advancing maternal age, and preexisting vascular disease (Kollmann et al., 2015), and include many risk factors that coexist in patients who also have a diagnosis of PCOS.
While several studies suggest that women with PCOS are at increased risk of developing preeclampsia (Alur-Gupta et al., 2021; Boomsa et al., 2006; Kjerulff et al., 2011; Manoharan and Wong, 2020; Mills et al., 2020; Mumm et al., 2015; Pan et al., 2020; Pan et al., 2021; Qin et al., 2013; Roos et al., 2011; Yu et al., 2016), others have found an association only with a hyperandrogenic or insulin resistant phenotype or failed to find an independent correlation (Bjercke et al., 2002; Haakova et al., 2003; Kollmann et al., 2015; Mikola et al., 2001; Naver et al., 2014; Turhan et al., 2003; Weerakiet at al., 2004). Some clinical features that have been found to confer increased risk for preeclampsia in patients with PCOS include overweight (Zhang et al., 2020), obesity (Christ et al., 2019; D’Alterio et al., 2021; Pan et al., 2021; Rantakallio et al., 2021), hyperandrogenism (Dehghani Firoozabadi et al., 2020; Pan et al., 2021; Valdimarsdottir et al., 2021), chronic anovulation (Dehghani Firoozabadi et al., 2020), and increased insulin resistance (Christ et al., 2019; D’Alterio et al., 2021; Pan et al., 2021). For instance, in a prospective cohort study of patients with PCOS, elevated body mass index (BMI) was found to be the strongest predictor of preeclampsia (Foroozanfard et al., 2020). Additionally, compared to pregnant women without PCOS, pregnant women with PCOS are more likely to be obese and nulliparous, and to have received fertility treatment—all of which have been independently associated with preeclampsia risk (Bartsch et al., 2016; Jackson et al., 2004; Schneider et al., 2019; Valdimarsdottir et al., 2021).
Given limitations in adjusting for confounders, heterogeneous patient populations, and publication bias in prior studies, the association between PCOS and preeclampsia remains unclear. The precise mechanism that links PCOS to preeclampsia remains unknown, although aberrant placental growth (Phipps et al., 2016; Young et al., 2010), vascular dysfunction (Phipps et al., 2016; Hu et al., 2007; Kelley et al., 2019; Wiegel et al., 2020; Young et al., 2010), and/or oxidative stress (Lu et al., 2018; Phipps et al., 2016) may play a role. This study aims to (1) quantify the association between PCOS and preeclampsia and (2) determine if certain clinical features of PCOS are associated with preeclampsia.
MATERIALS AND METHODS
Study Population
A retrospective case-control study of women with singleton deliveries was performed. Deliveries that occurred from January 1, 2011 through December 31, 2015 at a university-affiliated tertiary care hospital were included. This study was approved by the institutional review board at Beth Israel Deaconess Medical Center.
Cases and Controls
Cases of preeclampsia were identified through a prior study at this institution, and the methods have been previously described (Modest et al., 2019). Briefly, International Classification of Diseases (ICD9, ICD10) codes were used to identify potential cases of preeclampsia at delivery and confirmed by medical record review. Each delivery with preeclampsia (case) was matched to the next delivery without preeclampsia (control) within one week of gestational age.
PCOS
A medical record review was conducted on each case and control to assess for the presence of PCOS, including documented diagnosis of PCOS, as well as information on menstrual irregularity (oligomenorrhea or amenorrhea), clinical hyperandrogenism (acne or hirsutism), biochemical hyperandrogenism (dehydroepiandrosterone sulfate (DHEAS), sex hormone binding globulin, and testosterone values) and sonographic evidence of polycystic ovaries. A patient was considered to have a diagnosis of PCOS if they either (1) had a documented diagnosis of PCOS or (2) met the Rotterdam criteria with two of the following: menstrual irregularity, clinical or biochemical hyperandrogenism, and polycystic ovaries on ultrasound (Balen et al., 2016; Fauser et al., 2012)
Infertility diagnosis and mode of conception were also recorded. If the pregnancy was achieved through IVF, the oocyte source (autologous or donor) was recorded.
For each parameter, the absence of documentation affirming the presence of a specific condition was considered sufficient to indicate that the patient had not been diagnosed with that particular condition.
Covariates
Demographic information, including self-reported race and ethnicity, was electronically abstracted from billing and administrative databases and supplemented with birth certificate data from the Department of Public Health (DPH) where missing. Medical history and laboratory values were abstracted through a manual review of medical records. Maternal pre-pregnancy weight and BMI were obtained from DPH data. Information was collected regarding patient co-morbidities and risk factors for preeclampsia, including chronic hypertension, preeclampsia in a prior pregnancy, pre-gestational diabetes, gestational diabetes, renal disease, antiphospholipid antibody syndrome, systemic lupus erythematosus, and cigarette smoking. Gestational diabetes was considered to be present if there was documentation of this diagnosis in the medical record, with management as noted in subsequent clinic or labor & delivery records informing whether a patient had GDMA1 (diet-controlled) vs GDMA2 (medication-controlled). At the institution where this study was performed, gestational diabetes is diagnosed by oral glucose test typically conducted between 24–28 weeks gestation. Again, the absence of documentation affirming the presence of a specific condition was considered sufficient to indicate that the patient had not been diagnosed with that particular condition. The following laboratory values were recorded when available: hemoglobin A1c (HbA1c), thyroid-stimulating hormone (TSH), prolactin, anti-Mullerian hormone (AMH), follicle stimulating hormone (FSH), luteinizing hormone (LH), and estrogen (E2), testosterone, and DHEAS.
Statistical Analysis
A necessary sample size was calculated using a statistical power of 80% and p-value of 0.05. The prevalence of PCOS was assumed to be 10% in the preeclampsia group and 5% in controls, based on a 5% prevalence of PCOS in the general population and an odds ratio of approximately 2 for the development of preeclampsia in patients with PCOS as reported in several recent meta analyses (Boomsma et al., 2006; Bozdag et al., 2016; Kjerulff et al., 2011; Qin et al., 2013; Yu et al., 2016).10–13,42 Therefore, 435 cases and 435 controls were needed. Categorical variables were compared using Chi-square or Fisher’s exact tests. Continuous variables were compared using the Mann-Whitney U test. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). All models were adjusted for maternal age at delivery and race/ethnicity due to their association with PCOS and preeclampsia reported in the literature. Data are presented as median (interquartile range, IQR) or n (%).
Sensitivity Analyses
BMI and parity were of particular concern given their association with PCOS and preeclampsia individually, as well as their possible clinical significance in the association between preeclampsia and PCOS. However, they were not included in the statistical models described above due to concerns that BMI and parity were both mediators. Instead, two stratified analyses were conducted. The first was to assess the relationship between PCOS and preeclampsia within World Health Organization categories of BMI (<18 kg/m2, 18 - <25 kg/m2, 25 - <30 kg/m2, and 30+ kg/m2)(WHO, 2022). The second analysis was stratified by parity (nulliparous vs. parous). Of note, due to sparse data in the stratified analyses, the OR and 95% CI within strata of BMI were calculated using unconditional logistic regression adjusting for gestational age at delivery.
RESULTS
This study included 435 cases and 435 controls. The median patient age was 32.0 years (IQR 28.5–35.4) in cases and 32.5 years (IQR 29.2–35.5) in controls. While race and ethnicity differed between cases and controls, comorbidities, including history of pre-gestational diabetes, chronic hypertension, antiphospholipid antibody syndrome, lupus, and renal disease, were similar. The prevalence of infertility was similar between cases (15.9%) and controls (13.3%), and the use of IVF also was similar between cases (8.7%) and controls (5.7%). BMI was higher in cases (26.0 kg/m2, IQR 22.6 – 31.6) compared to controls (23.0 kg/m2, IQR 20.7 – 26.6). (Table 1)
Table 1.
Characteristics of Cases and Controls
| Preeclampsia n=435 | No preeclampsia n=435 | |
|---|---|---|
| Patient Demographics and Comorbidities | ||
| Age (years) | 32.0 (28.5–35.4) | 32.5 (29.2–35.5) |
| Unknown | 5 (1.1) | 6 (1.4) |
| 3+ | 116 (26.7) | 147 (33.8) |
| 3+ | 19 (4.4) | 20 (4.6) |
| Yes | 12 (7.9) | 14 (6.0) |
| Chronic hypertension | 54 (12.4) | 43 (9.9) |
| Type 2 | 25 (5.7) | 18 (4.1) |
| Antiphospholipid antibody syndrome | 3 (0.7) | 1 (0.2) |
| Renal disease | 7 (1.6) | 5 (1.1) |
| Lupus | 2 (0.5) | 1 (0.2) |
| Pregnancy Details | ||
| Unknown | 23 (5.3) | 18 (4.1) |
| Smoking in current pregnancy | 5 (1.1) | 9 (2.1) |
| None | 398 (91.5) | 401 (92.2) |
| Infertility | 69 (15.9) | 58 (13.3) |
| Other | 4 (0.9) | 7 (1.6) |
| Delivery Information | ||
| Gestational age at delivery | 37.4 (35.4–39.1) | 37.6 (35.3–39.0) |
| Cesarean | 243 (55.9) | 156 (35.9) |
| Birth weight (grams) | 2960 (2315–3465) | 2990 (2400–3460) |
BMI: Body mass index
GDMA1: gestational diabetes, diet controlled
GDMA2: gestational diabetes, medication controlled
Data are presented as median (interquartile range) or n (%)
Features of PCOS, including menstrual irregularity, hyperandrogenism, and polycystic ovaries also were similar between the two groups. Though patients with preeclampsia were more likely to have a diagnosis of PCOS (8.3%) than those without preeclampsia (6.2%), this difference was not statistically significant (adjusted OR 1.4, 95% CI 0.81–2.3). Compared to women without preeclampsia, women with preeclampsia did not have increased odds of menstrual irregularity (adjusted OR 1.04, 95% CI 0.61–1.8) or clinical or biochemical hyperandrogenism (adjusted OR 0.78, 95% CI 0.55–1.1). Women with preeclampsia were more likely to have polycystic ovaries than those without preeclampsia, however this association also was not statistically significant (adjusted OR 1.5, 95% CI 0.5–4.3). (Table 2)
Table 2.
Clinical Features of Polycystic Ovary Syndrome and Odds of Preeclampsia
| Preeclampsia n=435 | No preeclampsia n=435 | Odds ratio (95% CI) | Adjusted odds ratio (95% CI) | |
|---|---|---|---|---|
| Diagnosis of PCOS* | 36 (8.3) | 27 (6.2) | 1.3 (0.81 – 2.2) | 1.4 (0.81 – 2.3) |
| Documented PCOS | 30 (6.9) | 22 (5.1) | 1.4 (0.79 – 2.4) | 1.5 (0.83 – 2.6) |
| Amenorrhea | 4 (12.5) | 2 (6.9) | ||
| Androgenic alopecia | 3 (4.3) | 0 (0.0) | ||
| Elevated DHEAS¥ | 3 (0.7) | 1 (0.2) | 3.0 (0.31 – 28.8) | 2.5 (0.23 – 24.7) |
| Polycystic ovary morphology on ultrasound | 9 (2.1) | 6 (1.4) | 1.5 (0.53 – 4.2) | 1.5 (0.50 – 4.3) |
PCOS: Polycystic ovary syndrome; DHEAS: dehydroepiandrosterone sulfate Data presented as n (%) or odds ratio (OR) and 95% confidence interval (CI) Odds ratios adjusted for maternal age and race
Diagnosis of PCOS includes documented PCOS (explicit note of PCOS in the medical record) or 2 of the following Rotterdam criteria: hyperandrogenism, menstrual irregularity (oligomenorrhea or amenorrhea), polycystic ovaries on ultrasound
Total testosterone > 60 ng/dL or 2.1 nmol/L. Testosterone measurements available for 64 people
DHEAS > 395 mcg/dL or 10.6 umol/L. DHEAS measurements available for 58 people.
Sensitivity analyses explored whether particular subgroups of BMI and parity showed a stronger association between PCOS and preeclampsia (Table 3). Patients with preeclampsia were more likely to have PCOS if they were overweight (adjusted OR 2.3, 95% CI 0.73–7.0) or obese (adjusted OR 2.0, 95% CI 0.40–9.8) than patients with a normal BMI (adjusted OR 1.5, 95% CI 0.69–3.1), but these odds ratios remained not statistically significant. Nulliparous patients with preeclampsia did not have a statistically significant association with PCOS (adjusted OR 1.7, 95% CI 0.66–4.6), and neither did parous patients (adjusted OR 1.2, 95% CI 0.36 – 3.9). For both sensitivity analyses, stratification into sub-groups of BMI and parity resulted in a reduced sample size and thus wide confidence intervals, and no group showed a statistically significant association between PCOS and preeclampsia.
Table 3.
Sensitivity Analyses: Polycystic Ovary Syndrome and Odds of Preeclampsia Stratified by Body Mass Index and Parity
| Preeclampsia n=435 | No preeclampsia n=435 | Odds ratio (95% CI) | Adjusted odds ratio (95% CI) | |
|---|---|---|---|---|
| BMI categories | ||||
| Documented PCOS | 0 (0.0) | 1 (3.7) | - | - |
| Documented PCOS | 12 (6.7) | 14 (5.6) | 1.2 (0.55 – 2.7) | 1.4 (0.59 – 3.1) |
| Documented PCOS | 10 (10.5) | 3 (3.8) | 2.9 (0.76 – 10.9) | 3.2 (0.83 – 12.6) |
| Documented PCOS | 8 (6.0) | 2 (3.3) | 1.8 (0.36 – 8.7) | 1.7 (0.34 – 8.5) |
| Parity | ||||
| Documented PCOS | 5 (3.3) | 9 (3.9) | 1.6 (0.67 – 3.9) | 1.7 (0.66 – 4.6) |
| Documented PCOS | 25 (8.8) | 13 (6.4) | 1.3 (0.30 – 6.0) | 1.04 (0.22 – 4.9) |
PCOS: Polycystic ovary syndrome; BMI: Body mass index
Data presented as n (%) or odds ratio (OR) and 95% confidence interval (CI)
Odds ratios adjusted for maternal age and race. For BMI groups, odds ratios additionally adjusted for gestational age at delivery
Diagnosis of PCOS includes documentation of PCOS or 2 of the following Rotterdam criteria: hyperandrogenism, menstrual irregularity (oligomenorrhea or amenorrhea), polycystic ovaries on ultrasound
DISCUSSION
In this patient cohort, a prior confirmed diagnosis of PCOS did not significantly increase the risk of preeclampsia. These findings contradict recent meta-analyses, published between 2006 and 2021, in which women with PCOS had two- to four- times the odds of developing preeclampsia when compared to healthy controls (Table 4) (Boomsma et al., 2006; Kjerulff et al., 2011; Pan et al., 2021; Qin et al., 2013; Yu et al., 2016). These meta-analyses are frequently cited in the literature and inform how providers counsel PCOS patients on their increased risk of adverse pregnancy outcomes, including preeclampsia (Fauser et al., 2012; Teede et al., 2018). Notably, these meta-analyses include studies with variability both in characteristics of controls and in the criteria used to define PCOS; assess multiple outcomes with the association between PCOS and preeclampsia investigated as just one part of each meta-analysis; and differ in patient factors adjusted for and other statistical analysis methods used. In all five meta-analyses, the association between PCOS and preeclampsia was only statistically significant in a minority of the included studies, and the pooled results were driven by a small number of studies with a high odds ratio or that were more heavily weighted than the others. Further limitations in these studies, including heterogeneity in patient demographic characteristics, inability to adjust for potential confounders, and publication bias should raise caution when interpreting their results.
Table 4.
Characteristics of Previously Reported Meta-Analyses Showing an Association between PCOS and Preeclampsia.
| Meta-analysis | Characteristics of Studies Included and Sample Size | Definition of PCOS | Primary Outcome | Pooled Odds Ratio for PCOS and Preeclampsia | Distribution of statistically significant studies | Possible Confounders and Biases |
|---|---|---|---|---|---|---|
| Boomsma et al., 2006 | 15 studies included, with 720 women with PCOS and 4,505 women without PCOS Excluded studies if PCOS patients used metformin during pregnancy |
Rotterdam criteria | Gestational diabetes mellitus, pregnancy-induced hypertension, preeclampsia | OR 3.47, 95% CI 1.95–6.17 | 1/8 studies showing a statistically significant association, with an OR of 9.07 (same individual study as the study in Kjerulff et al., 2011 described below with an OR of 9.07) | “All studies in which preeclampsia was an end-point reported a lower parity, higher BMI or multiple pregnancies among women with PCOS versus controls. Therefore, no subgroup analysis on higher validity studies could be performed.” Variability in mode of conception and definition of control group across the studies included |
| Kjerulff et al., 2011 | 23 total studies for all primary outcomes, including 7 prospective studies, with 2,544 patients with PCOS and 89,848 patients without PCOS Excluded studies if PCOS patients used metformin after conception |
Rotterdam, or Androgen Excess and Polycystic Ovary Syndrome Society criteria | Gestational diabetes mellitus, pregnancy-induced hypertension, preeclampsia | OR 4.23, 95% CI 2.77–6.46 | 2/12 PCOS and preeclampsia studies showed a statistically significant association, but those that did had high odds ratios of 9.07 and 19.12 All PCOS and preeclampsia studies had small sample sizes with <100 PCOS patients each |
Marked variability in mode of conception, including spontaneous, ovulation induction, and use of assisted reproductive technology across all studies with distribution of mode of conception across cases and controls not specified |
| Qin et al., 2013 | 15 studies including 4,564 patients with PCOS and 1,194,098 patients without PCOS Excluded studies if patients had preexisting chronic hypertension or diabetes mellitus, if PCOS patients used metformin after conception, or if the multiple pregnancy rate was different between patients with and without PCOS |
As defined by individual studies based on NIH, Rotterdam, or Androgen Excess Society criteria | “Pregnancy outcomes” including “gestational diabetes mellitus (GDM), pregnancy-induced hypertension (PIH), preeclampsia, premature delivery, neonatal birth weight, caesarean section and admission to a neonatal intensive care unit (NICU).” | OR 3.28, 95% CI 2.06–5.22 | 4/15 studies showed a significant association between PCOS and preeclampsia. When “borderline eligible” studies were excluded, 3/14 studies showed a significant association with one study contributing to 90% weight |
The authors detected significant between-study heterogeneity, but a meta-regression revealed no significant association between heterogeneity and study design or BMI. Authors noted obesity was an independent risk factor for adverse pregnancy outcomes, and frequently occurs in patients with PCOS, but a meta regression studying “lean” vs “obese” patients was not able to be conducted. Marked variability in mode of conception and use of assisted reproductive technology between cases and controls, with most individual studies including patients in the PCOS group who conceived via assisted reproductive technology |
| Yu et al., 2016 | 40 observation al studies, including 15 prospective and 25 retrospective, with 17,816 pregnancies in patients with PCOS and 123,756 pregnancies in patients without PCOS | Not specified; likely as defined by included studies | “Pregnancy outcomes” including “gestational diabetes mellitus (GDM), preeclampsia, pregnancy-induced hypertension (PIH), preterm delivery, cesarean delivery, oligohydramnios, polyhydramnios, large for gestational age, small for gestational age, fetal growth restriction, miscarriage, preterm premature rupture membrane, fasting blood glucose, high density lipoprotein, low-density lipoprotein, triglyceride, total cholesterol, diastolic blood pressure, systolic blood pressure, congenital malformation, hypoglycemia, macrosomia, perinatal death, and respiratory distress syndrome.” | OR 2.79, 95% CI 2.29–3.38 | 9/25 of studies showed an association, with studies that had a significant association weighted more heavily than those without Statistically significant associations were seen in subgroup analyses based on study design, patient age, and pre-pregnancy BMI. |
Authors note significant publication bias for the relationship between PCOS and preeclampsia based on the results of Egger and Begg tests. |
| Pan et al., 2021 | 30 observation al studies including 68,419 pregnancies in patients with PCOS and 11,141,378 pregnancies in patients without PCOS Included “observational studies of all types, including cross-sectional, case-control, and cohort designs that included women with and without PCOS and that reported the relevant outcomes such as HDP, PIH, and PE.” |
As defined by individual studies, with differing definition of PCOS based on NIH, Rotterdam, or ICD criteria | Hypertensive disorders of pregnancy (HDP), including pregnancy-induced hypertension (PIH)and preeclampsia (PE) | OR 2.07, 95% CI 1.91–2.24 Of note, OR for HDP was 2.02, 95% CI 1.83–2.22. |
9/20 of studies investigating PCOS and preeclampsia showed an association, but those that did were weighted heavier than negative studies, with 3 studies with large sample sizes contributing to >50% of the pooled OR. Pooled odds ratio for PCOS and preeclampsia that was adjusted for age, BMI, and parity only showed statistical significance in 3/11 studies considered, but these 3 studies contributed to 89% of weight. |
No association between PCOS and HDP in subgroups of patients without hyperandroge nism and without gestational diabetes mellitus Authors conclude, based on the results of a metaregression, that “BMI contributed significantly to the heterogeneity in the prevalence of hypertensive disorders of pregnancy.” |
Limitations of our study include limited access to medical records outside the pregnancy and postpartum period, as well as issues inherent to confirming a clinical diagnosis through medical record review. Because PCOS is commonly diagnosed in adolescence, there may have been patients in this study who were diagnosed at another institution, whose records were unavailable for review. Additionally, this study assumed careful documentation of clinical features, such as oligomenorrhea, acne, hirsutism, and other clinical and laboratory evidence of hyperandrogenism. These subtle clinical features, which rely on careful history taking, physical examination, and documentation, may not have been captured by clinical encounters available in the electronic medical record system that was used. Other conditions implicated in both PCOS and preeclampsia, such as insulin resistance without frank diabetes mellitus, also may not have routinely been captured in the medical record. Features not documented were considered not present. These limitations may have led to patients with PCOS being classified as not having PCOS, and thus biasing the results towards the null.
Additionally, the patient population in this study received care at a tertiary, university-affiliated medical center, and therefore cases may have been more likely to have risk factors for preeclampsia other than PCOS; indeed, race/ethnicity, gravidity, parity and obesity were different between cases and controls. However, several comorbidities associated with preeclampsia including history of chronic hypertension, pre-gestational diabetes, gestational diabetes, age, and mode of conception were similar between cases and controls. Some of these comorbidities, such as chronic hypertension and pre-gestational diabetes mellitus may have been undiagnosed in patients who were asymptomatic and/or did not seek regular medical care prior to pregnancy. Another possible explanation for these unexpected similarities between groups is that in this study, cases and controls were matched by gestational age at delivery. The median gestational age at delivery was 37 weeks in both groups with an IQR between 35–39 weeks. Thus, this population includes fewer patients who were required to be delivered earlier in their pregnancies and who may have had either different risk factors for preeclampsia or more severe preeclampsia than the patients ultimately included in this case-control study. Finally, our a priori sample size calculation assumed that the PCOS group would have two times the odds of preeclampsia compared to the group without PCOS. In our study population, there was no difference in PCOS between the two groups despite PCOS including several conditions that are risk factors for preeclampsia, such as obesity, insulin resistance, abnormal cardiovascular markers, etc. This investigation was ultimately underpowered to detect a difference in preeclampsia in cases and controls due to overestimating the difference between groups.
Strengths of this study include a diverse patient population and medical record confirmation of cases and controls. The characteristics of the patients spanned a wide range of demographic characteristics and comorbidities that were similar between cases and controls, which minimizes confounding. Additionally, determination of a PCOS history incorporated the Rotterdam criteria, instead of only relying on an explicitly noted diagnosis of PCOS in the medical record.
Despite this broad definition of PCOS, this study did not detect a significantly increased risk of preeclampsia in patients meeting criteria for a diagnosis of PCOS, including in subgroups with additional risk factors such as elevated BMI and nulliparity. Given the limitation in sample size, particularly in subgroups with elevated BMI in which the association between PCOS and preeclampsia was stronger, further work is necessary to understand whether there is a separate biological mechanism in this group or whether there is an interaction effect between obesity and PCOS.
CONCLUSION
In this study, a history of PCOS was not significantly associated with the risk of preeclampsia. The association between PCOS and preeclampsia remains unclear, which may be due to heterogeneity in features of PCOS and continued conflicting results. Further investigation is necessary to determine if there are particular subgroups of patients with PCOS, such as those with biochemical hyperandrogenism or ovulatory dysfunction, who are consistently at increased risk for preeclampsia and if the association is related to obesity, insulin resistance, infertility, or other potential causal pathways.
HIGHLIGHTS.
Retrospective case-control study of 870 patients at an academic medical center
No significant difference in PCOS history in patients with and without preeclampsia
Stronger, but not significant, association between PCOS and preeclampsia if BMI ≥ 25
KEY MESSAGE.
This retrospective case-control study of 870 patients did not find an association between PCOS and preeclampsia. Results in subgroups suggest that there is a need for further work in particular populations of patients with PCOS who may be at increased risk of preeclampsia, such as those with higher BMI.
ACKNOWLEDGMENTS
This work was conducted with support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL1 TR002541) and financial contributions from Harvard University and its affiliated academic healthcare centers.
AUTHOR BIOGRAPHY
Ashwini Joshi, MD is a graduate of Harvard Medical School and a first-year resident physician in the Department of Anesthesia, Critical Care, and Pain Medicine at Massachusetts General Hospital. Her clinical and academic interests are in women’s health, critical care, and medical education.
Footnotes
Declarations of interest: none
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CRediT AUTHOR STATEMENT
Ashwini Joshi MD: conceptualization, methodology, investigation, data curation, writing – original draft, writing – review and editing
Ashley Aluko MD: conceptualization, methodology, software, investigation, data curation, writing – original draft, writing – review and editing
Aaron K. Styer MD: conceptualization, methodology, writing – review and editing
Brett C. Young MD: methodology, writing – review and editing
Katherine M. Johnson MD: methodology, data curation, writing – review and editing
Michele R. Hacker ScD MSPH: methodology, formal analysis, supervision, administration, writing – review and editing
Anna M. Modest PhD MPH: conceptualization, methodology, software, validation, formal analysis, resources, writing – original draft, writing – review and editing, visualization, supervision, administration
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